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values | src_encoding stringclasses 20
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value | is_vendor bool 2
classes | is_generated bool 2
classes | length_bytes int64 4 6.02M | extension stringclasses 78
values | content stringlengths 2 6.02M | authors listlengths 1 1 | author stringlengths 0 175 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
dc469d1c8508d7fcdba7a71ff8c53b945b1202e7 | a96333bb48c34d18b7a99b2c724655dbc1fe2dbb | /python/unityagents/brain.py | d2b16d0fcbb9163b2443b38bf6fb7f1bcb926f25 | [
"MIT"
] | permissive | udacity/deep-reinforcement-learning | cdcdbf5e315659d9980866560882930a433b9062 | 561eec3ae8678a23a4557f1a15414a9b076fdfff | refs/heads/master | 2023-07-08T06:33:54.653113 | 2021-12-06T22:42:31 | 2021-12-06T22:42:31 | 140,018,843 | 4,837 | 2,575 | MIT | 2023-06-28T19:36:57 | 2018-07-06T18:36:23 | Jupyter Notebook | UTF-8 | Python | false | false | 2,859 | py | from typing import Dict
class BrainInfo:
def __init__(self, visual_observation, vector_observation, text_observations, memory=None,
reward=None, agents=None, local_done=None,
vector_action=None, text_action=None, max_reached=None):
"""
Describes experience at current step of all agents linked to a brain.
"""
self.visual_observations = visual_observation
self.vector_observations = vector_observation
self.text_observations = text_observations
self.memories = memory
self.rewards = reward
self.local_done = local_done
self.max_reached = max_reached
self.agents = agents
self.previous_vector_actions = vector_action
self.previous_text_actions = text_action
AllBrainInfo = Dict[str, BrainInfo]
class BrainParameters:
def __init__(self, brain_name, brain_param):
"""
Contains all brain-specific parameters.
:param brain_name: Name of brain.
:param brain_param: Dictionary of brain parameters.
"""
self.brain_name = brain_name
self.vector_observation_space_size = brain_param["vectorObservationSize"]
self.num_stacked_vector_observations = brain_param["numStackedVectorObservations"]
self.number_visual_observations = len(brain_param["cameraResolutions"])
self.camera_resolutions = brain_param["cameraResolutions"]
self.vector_action_space_size = brain_param["vectorActionSize"]
self.vector_action_descriptions = brain_param["vectorActionDescriptions"]
self.vector_action_space_type = ["discrete", "continuous"][brain_param["vectorActionSpaceType"]]
self.vector_observation_space_type = ["discrete", "continuous"][brain_param["vectorObservationSpaceType"]]
def __str__(self):
return '''Unity brain name: {0}
Number of Visual Observations (per agent): {1}
Vector Observation space type: {2}
Vector Observation space size (per agent): {3}
Number of stacked Vector Observation: {4}
Vector Action space type: {5}
Vector Action space size (per agent): {6}
Vector Action descriptions: {7}'''.format(self.brain_name,
str(self.number_visual_observations),
self.vector_observation_space_type,
str(self.vector_observation_space_size),
str(self.num_stacked_vector_observations),
self.vector_action_space_type,
str(self.vector_action_space_size),
', '.join(self.vector_action_descriptions))
| [
"alexis.cook@acook-mbp.local"
] | alexis.cook@acook-mbp.local |
d528ca8589f7c3e2e6250446695ec6b52efd26a8 | 30c688a237481cf4e79675809fe3d6f862293644 | /read_data.py | 8d918eecb5385516e5ad89fe6c459fa849161c0a | [] | no_license | trishtzy/cs4242-assignment2 | 38ae92abccd27e9b992e5f88142f68b888974c52 | c0bcc4f67c0623dad41d863f5dad750818399de5 | refs/heads/master | 2021-03-27T12:02:40.172833 | 2017-03-22T00:15:08 | 2017-03-22T00:15:08 | 84,911,445 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,377 | py | import json
import logging
import os.path
from dateutil.parser import parse
import datetime
# create logger
logger = logging.getLogger('main.read_data')
def read_data(path, limit=-1):
"""Given a path to JSON file, load and return contents up to limit items."""
logger.debug("Reading " + path)
if (not os.path.isfile(path)):
logger.error("File " + path + " does not exist!")
return None
with open(path, 'r') as f:
data = json.load(f)
logger.debug("Loaded %d items" % len(data))
if limit < 0:
return data
count = 0
new_data = {}
for d in data:
new_data[d] = data[d]
count += 1
if count >= limit:
return new_data
return new_data
def read_tweet_content(tw_id):
"""Given tweet ID, load and return tweet contents"""
with open('tweets/'+tw_id+'.json', 'r') as f:
tw_content = json.load(f)
return tw_content
'Return the integer id of the root of the cascade (because 1 sometimes may be missing)'
def find_root_of_cascade(cascade):
tweets = cascade.keys()
return str(sorted(map(int, tweets))[0])
'''Get the label viral/non-viral (True or False) for a cascade.'''
def get_label(cascade, k):
return len(cascade) >= 2*k
def extract_labels(dataset, k):
"""Extract viral labels from dataset - WORKING FUNCTION"""
labels={}
for key in dataset:
labels[key] = get_label(dataset[key], k)
return labels
def parse_datetime(datetime_str):
"""Takes in datetime string in format "1285421777000" or "Tue Aug 31 14:57:43 +0000 2010"
and returns datetime object"""
try:
return datetime.datetime.fromtimestamp(int(datetime_str)/1000)
except ValueError:
return parse(datetime_str)
def json_datetime(obj):
"""JSON serializer for datetime objects
Change this to the required format for outputting json for evaluation"""
if isinstance(obj, datetime.datetime):
serial = obj.isoformat()
return serial
raise TypeError ("Type not serializable")
# if __name__ == '__main__':
# tr, ts = read_data('dataset/k4/training.json', 'dataset/k4/testing.json')
# inspect_data(tr, 'dataset/k4/root_tweet.json')
# social_network = read_social_network('social_network.json')
# ft = extract_example_features(tr, social_network)
# tr_labels = extract_labels(tr, 4)
| [
"jonathan@jgiam.com"
] | jonathan@jgiam.com |
6abf44749d4153b914aac2a178ca3a33b570c4e2 | ae051f86e69053302e733283f34e4dff898628c0 | /tests/fixtures/binomial_edge_model_fixtures.py | 5bdaefa53a250eaf54223aeb26c25eb793c6fb72 | [] | no_license | coast-team/abc-shadow | 1ba2f411fc1f65be81a6de797a0080db48b23cc2 | b0fb7d25e885c8908da01ca1bd3c20f269502003 | refs/heads/master | 2020-05-07T13:04:21.475288 | 2019-04-03T13:15:44 | 2019-04-03T13:15:44 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 191 | py | import pytest
from abc_shadow.model.binomial_edge_graph_model import BinomialEdgeGraphModel
@pytest.fixture
def get_empty_binomial_edge_model(request):
return BinomialEdgeGraphModel(0)
| [
"quentin.laporte-chabasse@inria.fr"
] | quentin.laporte-chabasse@inria.fr |
e26798e9f5defc36614767a02ffd42d319cc24be | 7ec0ce1c455daaa414137b0d1db88eb116dfd2a6 | /yt_concate/pipeline/steps/download_videos.py | a2d172add1e1e4b7a04143442e03615a410b6a35 | [] | no_license | lucaschen0706/yt_concate | f562a3ff607eb7631903f61ad1b669724628d383 | 674816dcff7d79fe12cc6d96afc2a8f5e452d6d7 | refs/heads/main | 2023-06-13T03:53:29.572180 | 2021-07-13T13:07:51 | 2021-07-13T13:07:51 | 382,047,346 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 611 | py | from .step import Step
from pytube import YouTube
from yt_concate.settings import VIDEOS_DIR
class DownloadVideos(Step):
def process(self, data, inputs, utils):
yt_set = set([found.yt for found in data])
print('videos to download=', len(yt_set))
for yt in yt_set:
url = yt.url
if utils.video_file_exist(yt):
print(f'found existing video file for {url}, skipping')
continue
print('downloading', url)
YouTube(url).streams.first().download(output_path=VIDEOS_DIR, filename=yt.id)
return data | [
"vincent.yeats@gmail.com"
] | vincent.yeats@gmail.com |
98239664e3b9f688468eb75fcbeb94b3f8f3ea24 | 72644f1098f4b6703cdabb66b4aa91d54a911cbe | /src/protocol/protobuf_client.py | 1b446a6bef96243d1614b55c3c19ed43565d226c | [] | no_license | fei-cow/galaxy-integration-steam | 8f762b5ccbfa2bc8b0f929d0d26fd9ab3802ac94 | b638dd39e95647236ed22c493437f300595eb90b | refs/heads/master | 2022-10-08T16:51:40.050633 | 2020-06-09T13:07:19 | 2020-06-09T13:07:19 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 29,453 | py | import asyncio
import struct
import gzip
import json
import logging
import socket
from itertools import count
from typing import Awaitable, Callable,Dict, Optional, Any
from galaxy.api.errors import UnknownBackendResponse
from typing import List, NamedTuple
from protocol.messages import steammessages_base_pb2, steammessages_clientserver_login_pb2, steammessages_player_pb2, \
steammessages_clientserver_friends_pb2, steammessages_clientserver_pb2, steamui_libraryroot_pb2, steammessages_clientserver_2_pb2
from protocol.consts import EMsg, EResult, EAccountType, EFriendRelationship, EPersonaState
from protocol.types import SteamId, ProtoUserInfo
import vdf
import hashlib
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
class SteamLicense(NamedTuple):
license: str
shared: bool
class ProtobufClient:
_PROTO_MASK = 0x80000000
_ACCOUNT_ID_MASK = 0x0110000100000000
def __init__(self, set_socket):
self._socket = set_socket
self.log_on_handler: Optional[Callable[[EResult], Awaitable[None]]] = None
self.log_off_handler: Optional[Callable[[EResult], Awaitable[None]]] = None
self.relationship_handler: Optional[Callable[[bool, Dict[int, EFriendRelationship]], Awaitable[None]]] = None
self.user_info_handler: Optional[Callable[[int, ProtoUserInfo], Awaitable[None]]] = None
self.user_nicknames_handler: Optional[Callable[[dict], Awaitable[None]]] = None
self.license_import_handler: Optional[Callable[[int], Awaitable[None]]] = None
self.app_info_handler: Optional[Callable[[int, str], Awaitable[None]]] = None
self.package_info_handler: Optional[Callable[[int], Awaitable[None]]] = None
self.translations_handler: Optional[Callable[[int, Any], Awaitable[None]]] = None
self.stats_handler: Optional[Callable[[int, Any, Any], Awaitable[None]]] = None
self.user_authentication_handler: Optional[Callable[[str, Any], Awaitable[None]]] = None
self.sentry: Optional[Callable[[], Awaitable[None]]] = None
self.steam_id: Optional[int] = None
self.times_handler: Optional[Callable[[int, int, int], Awaitable[None]]] = None
self.times_import_finished_handler: Optional[Callable[[bool], Awaitable[None]]] = None
self._heartbeat_task: Optional[asyncio.Task] = None
self._session_id: Optional[int] = None
self._job_id_iterator = count(1)
self.job_list = []
self.account_info_retrieved = asyncio.Event()
self.login_key_retrieved = asyncio.Event()
self.collections = {'event': asyncio.Event(),
'collections': dict()}
async def close(self, is_socket_connected):
if is_socket_connected:
await self.send_log_off_message()
if self._heartbeat_task is not None:
self._heartbeat_task.cancel()
async def wait_closed(self):
pass
async def _process_packets(self):
pass
async def run(self):
while True:
for job in self.job_list.copy():
logger.info(f"New job on list {job}")
if job['job_name'] == "import_game_stats":
await self._import_game_stats(job['game_id'])
self.job_list.remove(job)
if job['job_name'] == "import_collections":
await self._import_collections()
self.job_list.remove(job)
if job['job_name'] == "import_game_times":
await self._import_game_time()
self.job_list.remove(job)
try:
packet = await asyncio.wait_for(self._socket.recv(), 0.1)
await self._process_packet(packet)
except asyncio.TimeoutError:
pass
async def send_log_off_message(self):
message = steammessages_clientserver_login_pb2.CMsgClientLogOff()
logger.info("Sending log off message")
try:
await self._send(EMsg.ClientLogOff, message)
except Exception as e:
logger.info(f"Unable to send logoff message {repr(e)}")
async def log_on_web_auth(self, steam_id, miniprofile_id, account_name, token):
# magic numbers taken from JavaScript Steam client
message = steammessages_clientserver_login_pb2.CMsgClientLogon()
message.account_name = account_name
message.protocol_version = 65580
message.qos_level = 2
message.client_os_type = 4294966596
message.ui_mode = 4
message.chat_mode = 2
message.web_logon_nonce = token
message.client_instance_id = 0
try:
self.steam_id = steam_id
await self.user_authentication_handler('steam_id', self.steam_id)
await self.user_authentication_handler('account_id', miniprofile_id)
await self._send(EMsg.ClientLogon, message)
except Exception:
self.steam_id = None
raise
async def log_on_password(self, account_name, password, two_factor, two_factor_type):
def sanitize_password(password):
return ''.join([i if ord(i) < 128 else '' for i in password])
message = steammessages_clientserver_login_pb2.CMsgClientLogon()
message.account_name = account_name
message.protocol_version = 65580
message.password = sanitize_password(password)
message.should_remember_password = True
message.supports_rate_limit_response = True
message.obfuscated_private_ip.v4 = struct.unpack(">L", socket.inet_aton(socket.gethostbyname(socket.gethostname())))[0] ^ 0xF00DBAAD
if two_factor:
if two_factor_type == 'email':
message.auth_code = two_factor
elif two_factor_type == 'mobile':
message.two_factor_code = two_factor
logger.info("Sending log on message using credentials")
await self._send(EMsg.ClientLogon, message)
async def log_on_token(self, steam_id, account_name, token, used_server_cell_id):
message = steammessages_clientserver_login_pb2.CMsgClientLogon()
message.account_name = account_name
message.cell_id = used_server_cell_id
message.protocol_version = 65580
message.should_remember_password = True
message.supports_rate_limit_response = True
message.login_key = token
message.obfuscated_private_ip.v4 = struct.unpack(">L", socket.inet_aton(socket.gethostbyname(socket.gethostname())))[0] ^ 0xF00DBAAD
sentry = await self.sentry()
if sentry:
logger.info("Sentry present")
message.eresult_sentryfile = EResult.OK
message.sha_sentryfile = sentry
self.steam_id = steam_id
logger.info("Sending log on message using token")
await self._send(EMsg.ClientLogon, message)
async def _import_game_stats(self, game_id):
logger.info(f"Importing game stats for {game_id}")
message = steammessages_clientserver_pb2.CMsgClientGetUserStats()
message.game_id = int(game_id)
await self._send(EMsg.ClientGetUserStats, message)
async def _import_game_time(self):
logger.info("Importing game times")
job_id = next(self._job_id_iterator)
message = steammessages_player_pb2.CPlayer_CustomGetLastPlayedTimes_Request()
message.min_last_played = 0
await self._send(EMsg.ServiceMethodCallFromClient, message, job_id, None, "Player.ClientGetLastPlayedTimes#1")
async def set_persona_state(self, state):
message = steammessages_clientserver_friends_pb2.CMsgClientChangeStatus()
message.persona_state = state
await self._send(EMsg.ClientChangeStatus, message)
async def get_friends_statuses(self):
job_id = next(self._job_id_iterator)
message = steamui_libraryroot_pb2.CChat_RequestFriendPersonaStates_Request()
await self._send(EMsg.ServiceMethodCallFromClient, message, job_id, None, "Chat.RequestFriendPersonaStates#1")
async def get_user_infos(self, users, flags):
message = steammessages_clientserver_friends_pb2.CMsgClientRequestFriendData()
message.friends.extend(users)
message.persona_state_requested = flags
await self._send(EMsg.ClientRequestFriendData, message)
async def _import_collections(self):
job_id = next(self._job_id_iterator)
message = steamui_libraryroot_pb2.CCloudConfigStore_Download_Request()
message_inside = steamui_libraryroot_pb2.CCloudConfigStore_NamespaceVersion()
message_inside.enamespace = 1
message.versions.append(message_inside)
await self._send(EMsg.ServiceMethodCallFromClient, message, job_id, None, "CloudConfigStore.Download#1")
async def get_packages_info(self, steam_licenses: List[SteamLicense]):
logger.info(f"Sending call {EMsg.PICSProductInfoRequest} with "
f"{[steam_license.license.package_id for steam_license in steam_licenses]}")
message = steammessages_clientserver_pb2.CMsgClientPICSProductInfoRequest()
for steam_license in steam_licenses:
info = message.packages.add()
info.packageid = steam_license.license.package_id
info.access_token = steam_license.license.access_token
await self._send(EMsg.PICSProductInfoRequest, message)
async def get_apps_info(self, app_ids):
logger.info(f"Sending call {EMsg.PICSProductInfoRequest} with {app_ids}")
message = steammessages_clientserver_pb2.CMsgClientPICSProductInfoRequest()
for app_id in app_ids:
info = message.apps.add()
info.appid = app_id
await self._send(EMsg.PICSProductInfoRequest, message)
async def get_presence_localization(self, appid, language='english'):
logger.info(f"Sending call for rich presence localization with {appid}, {language}")
message = steamui_libraryroot_pb2.CCommunity_GetAppRichPresenceLocalization_Request()
message.appid = appid
message.language = language
job_id = next(self._job_id_iterator)
await self._send(EMsg.ServiceMethodCallFromClient, message, job_id, None, target_job_name='Community.GetAppRichPresenceLocalization#1')
async def accept_update_machine_auth(self,jobid_target, sha_hash, offset, filename, cubtowrite):
logger.info("Accepting update machine auth")
message = steammessages_clientserver_2_pb2.CMsgClientUpdateMachineAuthResponse()
message.filename = filename
message.eresult = EResult.OK
message.sha_file = sha_hash
message.getlasterror = 0
message.offset = offset
message.cubwrote = cubtowrite
await self._send(EMsg.ClientUpdateMachineAuthResponse, message, None, jobid_target, None)
async def accept_new_login_token(self, unique_id, jobid_target):
logger.info("Accepting new login key")
message = steammessages_clientserver_login_pb2.CMsgClientNewLoginKeyAccepted()
message.unique_id = unique_id
await self._send(EMsg.ClientNewLoginKeyAccepted, message, None, jobid_target, None)
async def _send(
self,
emsg,
message,
source_job_id=None,
target_job_id=None,
target_job_name=None
):
proto_header = steammessages_base_pb2.CMsgProtoBufHeader()
if self.steam_id is not None:
proto_header.steamid = self.steam_id
else:
proto_header.steamid = 0 + self._ACCOUNT_ID_MASK
if self._session_id is not None:
proto_header.client_sessionid = self._session_id
if source_job_id is not None:
proto_header.jobid_source = source_job_id
if target_job_id is not None:
proto_header.jobid_target = target_job_id
if target_job_name is not None:
proto_header.target_job_name = target_job_name
header = proto_header.SerializeToString()
body = message.SerializeToString()
data = struct.pack("<2I", emsg | self._PROTO_MASK, len(header))
data = data + header + body
logger.info("Sending message %d (%d bytes)", emsg, len(data))
await self._socket.send(data)
logger.info("Send message success")
async def _heartbeat(self, interval):
message = steammessages_clientserver_login_pb2.CMsgClientHeartBeat()
while True:
await asyncio.sleep(interval)
await self._send(EMsg.ClientHeartBeat, message)
async def _process_packet(self, packet):
package_size = len(packet)
logger.info("Processing packet of %d bytes", package_size)
if package_size < 8:
logger.warning("Package too small, ignoring...")
raw_emsg = struct.unpack("<I", packet[:4])[0]
emsg = raw_emsg & ~self._PROTO_MASK
if raw_emsg & self._PROTO_MASK != 0:
header_len = struct.unpack("<I", packet[4:8])[0]
header = steammessages_base_pb2.CMsgProtoBufHeader()
header.ParseFromString(packet[8:8 + header_len])
if self._session_id is None and header.client_sessionid != 0:
logger.info("Session id: %d", header.client_sessionid)
self._session_id = header.client_sessionid
await self._process_message(emsg, header, packet[8 + header_len:])
else:
logger.warning("Packet with extended header - ignoring")
async def _process_message(self, emsg, header, body):
logger.info("Processing message %d", emsg)
if emsg == EMsg.Multi:
await self._process_multi(body)
elif emsg == EMsg.ClientLogOnResponse:
await self._process_client_log_on_response(body)
elif emsg == EMsg.ClientLogOff:
await self._process_client_log_off(body)
elif emsg == EMsg.ClientFriendsList:
await self._process_client_friend_list(body)
elif emsg == EMsg.ClientPersonaState:
await self._process_client_persona_state(body)
elif emsg == EMsg.ClientLicenseList:
await self._process_license_list(body)
elif emsg == EMsg.PICSProductInfoResponse:
await self._process_package_info_response(body)
elif emsg == EMsg.ClientGetUserStatsResponse:
await self._process_user_stats_response(body)
elif emsg == EMsg.ClientAccountInfo:
await self._process_account_info(body)
elif emsg == EMsg.ClientNewLoginKey:
await self._process_client_new_login_key(body, header.jobid_source)
elif emsg == EMsg.ClientUpdateMachineAuth:
await self._process_client_update_machine_auth(body, header.jobid_source)
elif emsg == EMsg.ClientPlayerNicknameList:
await self._process_user_nicknames(body)
elif emsg == EMsg.ServiceMethod:
await self._process_service_method_response(header.target_job_name, header.jobid_target, body)
elif emsg == EMsg.ServiceMethodResponse:
await self._process_service_method_response(header.target_job_name, header.jobid_target, body)
else:
logger.warning("Ignored message %d", emsg)
async def _process_multi(self, body):
logger.info("Processing message Multi")
message = steammessages_base_pb2.CMsgMulti()
message.ParseFromString(body)
if message.size_unzipped > 0:
loop = asyncio.get_running_loop()
data = await loop.run_in_executor(None, gzip.decompress, message.message_body)
else:
data = message.message_body
data_size = len(data)
offset = 0
size_bytes = 4
while offset + size_bytes <= data_size:
size = struct.unpack("<I", data[offset:offset + size_bytes])[0]
await self._process_packet(data[offset + size_bytes:offset + size_bytes + size])
offset += size_bytes + size
logger.info("Finished processing message Multi")
async def _process_client_log_on_response(self, body):
logger.info("Processing message ClientLogOnResponse")
message = steammessages_clientserver_login_pb2.CMsgClientLogonResponse()
message.ParseFromString(body)
result = message.eresult
if result == EResult.AccountLogonDenied:
if message.email_domain:
await self.user_authentication_handler('two_step', 'email')
if result == EResult.AccountLoginDeniedNeedTwoFactor:
await self.user_authentication_handler('two_step', 'mobile')
if result == EResult.OK:
interval = message.out_of_game_heartbeat_seconds
self.steam_id = message.client_supplied_steamid
await self.user_authentication_handler('steam_id', self.steam_id)
await self.user_authentication_handler('account_id', message.client_supplied_steamid - self._ACCOUNT_ID_MASK)
self._heartbeat_task = asyncio.create_task(self._heartbeat(interval))
else:
logger.info(f"Failed to log on, reason : {message}")
if self.log_on_handler is not None:
await self.log_on_handler(result)
async def _process_client_update_machine_auth(self, body, jobid_source):
logger.info("Processing message ClientUpdateMachineAuth")
message = steammessages_clientserver_2_pb2.CMsgClientUpdateMachineAuth()
message.ParseFromString(body)
sentry_sha = hashlib.sha1(message.bytes).digest()
await self.user_authentication_handler('sentry', sentry_sha)
await self.accept_update_machine_auth(jobid_source, sentry_sha, message.offset, message.filename, message.cubtowrite)
async def _process_account_info(self, body):
logger.info("Processing message ClientAccountInfo")
message = steammessages_clientserver_login_pb2.CMsgClientAccountInfo()
message.ParseFromString(body)
await self.user_authentication_handler('persona_name', message.persona_name)
self.account_info_retrieved.set()
async def _process_client_new_login_key(self, body, jobid_source):
logger.info("Processing message ClientNewLoginKey")
message = steammessages_clientserver_login_pb2.CMsgClientNewLoginKey()
message.ParseFromString(body)
await self.user_authentication_handler('token', message.login_key)
await self.accept_new_login_token(message.unique_id, jobid_source)
self.login_key_retrieved.set()
async def _process_client_log_off(self, body):
logger.info("Processing message ClientLoggedOff")
message = steammessages_clientserver_login_pb2.CMsgClientLoggedOff()
message.ParseFromString(body)
result = message.eresult
assert self._heartbeat_task is not None
self._heartbeat_task.cancel()
if self.log_off_handler is not None:
await self.log_off_handler(result)
async def _process_user_nicknames(self, body):
logger.info("Processing message ClientPlayerNicknameList")
message = steammessages_clientserver_friends_pb2.CMsgClientPlayerNicknameList()
message.ParseFromString(body)
nicknames = {}
for player_nickname in message.nicknames:
nicknames[str(player_nickname.steamid)] = player_nickname.nickname
await self.user_nicknames_handler(nicknames)
async def _process_client_friend_list(self, body):
logger.info("Processing message ClientFriendsList")
if self.relationship_handler is None:
return
message = steammessages_clientserver_friends_pb2.CMsgClientFriendsList()
message.ParseFromString(body)
friends = {}
for relationship in message.friends:
steam_id = relationship.ulfriendid
details = SteamId.parse(steam_id)
if details.type_ == EAccountType.Individual:
friends[steam_id] = EFriendRelationship(relationship.efriendrelationship)
await self.relationship_handler(message.bincremental, friends)
async def _process_client_persona_state(self, body):
logger.info("Processing message ClientPersonaState")
if self.user_info_handler is None:
return
message = steammessages_clientserver_friends_pb2.CMsgClientPersonaState()
message.ParseFromString(body)
for user in message.friends:
user_id = user.friendid
if user_id == self.steam_id and int(user.game_played_app_id) != 0:
await self.get_apps_info([int(user.game_played_app_id)])
user_info = ProtoUserInfo()
if user.HasField("player_name"):
user_info.name = user.player_name
if user.HasField("avatar_hash"):
user_info.avatar_hash = user.avatar_hash
if user.HasField("persona_state"):
user_info.state = EPersonaState(user.persona_state)
if user.HasField("gameid"):
user_info.game_id = user.gameid
rich_presence: Dict[str, str] = {}
for element in user.rich_presence:
if type(element.value) == bytes:
logger.info(f"Unsuported presence type: {type(element.value)} {element.value}")
rich_presence = {}
break
rich_presence[element.key] = element.value
if element.key == 'status' and element.value:
if "#" in element.value:
await self.translations_handler(user.gameid)
if element.key == 'steam_display' and element.value:
if "#" in element.value:
await self.translations_handler(user.gameid)
user_info.rich_presence = rich_presence
if user.HasField("game_name"):
user_info.game_name = user.game_name
await self.user_info_handler(user_id, user_info)
async def _process_license_list(self, body):
logger.info("Processing message ClientLicenseList")
if self.license_import_handler is None:
return
message = steammessages_clientserver_pb2.CMsgClientLicenseList()
message.ParseFromString(body)
licenses_to_check = []
for license in message.licenses:
# license.type 1024 = free games
# license.flags 520 = unidentified trash entries (games which are not owned nor are free)
if int(license.flags) == 520:
continue
if license.package_id == 0:
# Packageid 0 contains trash entries for every user
logger.info("Skipping packageid 0 ")
continue
if int(license.owner_id) == int(self.steam_id - self._ACCOUNT_ID_MASK):
logger.info(f"Owned license {license.package_id}")
licenses_to_check.append(SteamLicense(license=license, shared=False))
else:
if license.package_id in licenses_to_check:
continue
logger.info(f"Shared license {license.package_id}")
licenses_to_check.append(SteamLicense(license=license, shared=True))
await self.license_import_handler(licenses_to_check)
async def _process_package_info_response(self, body):
logger.info("Processing message PICSProductInfoResponse")
message = steammessages_clientserver_pb2.CMsgClientPICSProductInfoResponse()
message.ParseFromString(body)
apps_to_parse = []
for info in message.packages:
await self.package_info_handler()
package_id = str(info.packageid)
package_content = vdf.binary_loads(info.buffer[4:])
package = package_content.get(package_id)
if package is None:
continue
for app in package['appids'].values():
appid = str(app)
await self.app_info_handler(package_id=package_id, appid=appid)
apps_to_parse.append(app)
for info in message.apps:
app_content = vdf.loads(info.buffer[:-1].decode('utf-8', 'replace'))
appid = str(app_content['appinfo']['appid'])
try:
type_ = app_content['appinfo']['common']['type'].lower()
title = app_content['appinfo']['common']['name']
if type == 'game':
logger.info(f"Retrieved game {title}")
await self.app_info_handler(appid=appid, title=title, type=type_)
except KeyError:
logger.info(f"Unrecognized app structure {app_content}")
await self.app_info_handler(appid=appid, title='unknown', type='unknown')
if len(apps_to_parse) > 0:
logger.info(f"Apps to parse {apps_to_parse}, {len(apps_to_parse)} entries")
await self.get_apps_info(apps_to_parse)
async def _process_rich_presence_translations(self, body):
message = steamui_libraryroot_pb2.CCommunity_GetAppRichPresenceLocalization_Response()
message.ParseFromString(body)
logger.info(f"Received information about rich presence translations for {message.appid}")
await self.translations_handler(message.appid, message.token_lists)
async def _process_user_stats_response(self, body):
logger.info("Processing message ClientGetUserStatsResponse")
message = steammessages_clientserver_pb2.CMsgClientGetUserStatsResponse()
message.ParseFromString(body)
game_id = message.game_id
stats = message.stats
achievs = message.achievement_blocks
logger.info(f"Processing user stats response for {message.game_id}")
achievements_schema = vdf.binary_loads(message.schema,merge_duplicate_keys=False)
achievements_unlocked = []
for achievement_block in achievs:
achi_block_enum = 32 * (achievement_block.achievement_id - 1)
for index, unlock_time in enumerate(achievement_block.unlock_time):
if unlock_time > 0:
if str(achievement_block.achievement_id) not in achievements_schema[str(game_id)]['stats'] or \
str(index) not in achievements_schema[str(game_id)]['stats'][str(achievement_block.achievement_id)]['bits']:
logger.info("Non existent achievement unlocked")
continue
try:
if 'english' in achievements_schema[str(game_id)]['stats'][str(achievement_block.achievement_id)]['bits'][str(index)]['display']['name']:
name = achievements_schema[str(game_id)]['stats'][str(achievement_block.achievement_id)]['bits'][str(index)]['display']['name']['english']
else:
name = achievements_schema[str(game_id)]['stats'][str(achievement_block.achievement_id)]['bits'][str(index)]['display']['name']
achievements_unlocked.append({'id': achi_block_enum+index,
'unlock_time': unlock_time,
'name': name})
except:
logger.info(f"Unable to parse achievement {index} from block {achievement_block.achievement_id}")
logger.info(achievs)
logger.info(achievements_schema)
logger.info(message.schema)
raise UnknownBackendResponse()
await self.stats_handler(game_id, stats, achievements_unlocked)
async def _process_user_time_response(self, body):
logger.info("Received information about game times")
message = steammessages_player_pb2.CPlayer_CustomGetLastPlayedTimes_Response()
message.ParseFromString(body)
for game in message.games:
logger.info(f"Processing game times for game {game.appid}, playtime: {game.playtime_forever} last time played: {game.last_playtime}")
await self.times_handler(game.appid, game.playtime_forever, game.last_playtime)
await self.times_import_finished_handler(True)
async def _process_collections_response(self, body):
message = steamui_libraryroot_pb2.CCloudConfigStore_Download_Response()
message.ParseFromString(body)
for data in message.data:
for entry in data.entries:
try:
loaded_val = json.loads(entry.value)
self.collections['collections'][loaded_val['name']] = loaded_val['added']
except:
pass
self.collections['event'].set()
async def _process_service_method_response(self, target_job_name, target_job_id, body):
logger.info("Processing message ServiceMethodResponse %s", target_job_name)
if target_job_name == 'Community.GetAppRichPresenceLocalization#1':
await self._process_rich_presence_translations(body)
if target_job_name == 'Player.ClientGetLastPlayedTimes#1':
await self._process_user_time_response(body)
if target_job_name == 'CloudConfigStore.Download#1':
await self._process_collections_response(body)
| [
"friendsofgalaxy@gmail.com"
] | friendsofgalaxy@gmail.com |
761eeaa6e8e18f8112e281af167a7ccbc3748013 | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p03837/s920938245.py | 3aeb662f91dd2f46bbfdb9f9b7edc7cc0fdcb132 | [] | no_license | Aasthaengg/IBMdataset | 7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901 | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | refs/heads/main | 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 650 | py | def warshall_floyd():
for k in range(N):
for i in range(N):
for j in range(N):
d[i][j] = min(d[i][j], d[i][k]+d[k][j])
N, M = map(int, input().split())
d = [[10**18]*N for _ in range(N)]
for i in range(N):
d[i][i] = 0
edges = []
for _ in range(M):
a, b, c = map(int, input().split())
d[a-1][b-1] = c
d[b-1][a-1] = c
edges.append((a-1, b-1, c))
warshall_floyd()
ans = 0
for a, b, c in edges:
flag = True
for i in range(N):
for j in range(N):
if d[i][a]+c+d[b][j]==d[i][j]:
flag = False
if flag:
ans += 1
print(ans) | [
"66529651+Aastha2104@users.noreply.github.com"
] | 66529651+Aastha2104@users.noreply.github.com |
0e5dc5c575994fb9d51f5fd31c55ef92cd32e3f8 | ddadba7ebb64c2f341280728fd50e62369d6251e | /apps/notes_app/models.py | d5bba0cd359c70e4d2e127891182602fdd6e7910 | [] | no_license | LisCoding/Notes-App | 0f630b8229553d6cac278650f5649a9737ce1285 | 21bd8d0177ecf69335ec24e52c49df81f555f7a5 | refs/heads/master | 2021-06-22T10:44:25.755893 | 2017-08-31T19:33:49 | 2017-08-31T19:33:49 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 330 | py | from __future__ import unicode_literals
from django.db import models
# Create your models here.
class Note(models.Model):
title = models.CharField(max_length=255)
description = models.TextField(default="")
created_at = models.DateTimeField(auto_now_add = True)
updated_at = models.DateTimeField(auto_now = True)
| [
"cardozoliseth@gmail.com"
] | cardozoliseth@gmail.com |
74fc280f27c08e1336a10b2c6a6e61901d2387e1 | cd2ea0b9f0f8e01950ea4dd629a325ef26f914ad | /topics/Trees/BinaryTreeTraversal.py | b180ed30d3f5c5183418e0a9355c08ce008c8282 | [] | no_license | akhandsingh17/assignments | df5f1af44486ffefe1fefcccc643e6818ac1c55d | c89f40dcd7a8067fa78ed95d3fecc36cb1ca7b5d | refs/heads/master | 2023-08-24T18:00:32.938254 | 2021-10-06T06:01:32 | 2021-10-06T06:01:32 | 305,913,409 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,913 | py |
"""
1
/ \
2 3
/ \ / \
4 5 6 7
\
8
"""
class Node:
def __init__(self, val, left=None, right=None):
self.val = val
self.left = left
self.right = right
class BinaryTreeTraversal:
def __init__(self, root):
self.root = Node(root)
def preorder(self, start, traversal):
if start != None:
traversal = traversal + (str(start.val) + '-')
traversal = self.preorder(start.left, traversal )
traversal = self.preorder(start.right, traversal)
return traversal
def inorder(self, start, traversal):
if start != None:
traversal = self.preorder(start.left, traversal)
traversal = traversal + (str(start.val) + '-')
traversal = self.preorder(start.right, traversal)
return traversal
def postorder(self, start, traversal):
if start != None:
traversal = self.preorder(start.left, traversal )
traversal = self.preorder(start.right, traversal)
traversal = traversal + (str(start.val) + '-')
return traversal
def print_traversal(self, type):
if type == 'preorder':
return self.preorder(self.root, '')
if type == 'inorder':
return self.inorder(self.root, '')
if type == 'postorder':
return self.postorder(self.root, '')
def main():
tree = BinaryTreeTraversal(1)
tree.root.left = Node(2)
tree.root.right = Node(3)
tree.root.left.left = Node(4)
tree.root.left.right = Node(5)
tree.root.right.left = Node(6)
tree.root.right.right = Node(7)
tree.root.right.right.right = Node(8)
print(tree.print_traversal('preorder'))
print(tree.print_traversal('inorder'))
print(tree.print_traversal('postorder'))
if __name__=='__main__':
main()
| [
"akhans@amazon.com"
] | akhans@amazon.com |
781ee264796e64ff53334b63df8e2b3568dff462 | 7e0393251012e91213dddfd9c93f6b6b73ca2bfe | /cloudnetpy/products/drizzle_error.py | 6ac4ba7a3ba679b8c9adeb0aead54d7fe56fdbce | [
"MIT"
] | permissive | josephhardinee/cloudnetpy | ff4cc0303d7f2ae40f2d3466298257659ff3ccde | c37760db3cdfe62ae769f8090ba621803ec9a92c | refs/heads/master | 2021-03-06T15:37:51.529776 | 2020-02-13T09:05:29 | 2020-02-13T09:05:29 | 246,207,849 | 0 | 0 | MIT | 2020-03-10T04:29:48 | 2020-03-10T04:26:16 | null | UTF-8 | Python | false | false | 5,294 | py | import numpy as np
import numpy.ma as ma
import cloudnetpy.utils as utils
def _get_drizzle_indices(diameter):
return {'drizzle': diameter > 0,
'small': np.logical_and(diameter <= 1e-4, diameter > 1e-5),
'tiny': np.logical_and(diameter <= 1e-5, diameter > 0)}
def _read_input_uncertainty(categorize, uncertainty_type):
return tuple(db2lin(categorize.getvar(f'{key}_{uncertainty_type}'))
for key in ('Z', 'beta'))
MU_ERROR = 0.07
MU_ERROR_SMALL = 0.25
def get_drizzle_error(categorize, drizzle_parameters):
""" Estimates error and bias for drizzle classification.
Args:
categorize (DrizzleSource): The :class:`DrizzleSource` instance.
drizzle_parameters (DrizzleSolving): The :class:`DrizzleSolving` instance.
Returns:
errors (dict): Dictionary containing information of estimated error and bias for drizzle
"""
parameters = drizzle_parameters.params
drizzle_indices = _get_drizzle_indices(parameters['Do'])
error_input = _read_input_uncertainty(categorize, 'error')
bias_input = _read_input_uncertainty(categorize, 'bias')
errors = _calc_errors(drizzle_indices, error_input, bias_input)
return errors
def _calc_errors(drizzle_indices, error_input, bias_input):
errors = _calc_parameter_errors(drizzle_indices, error_input)
biases = _calc_parameter_biases(bias_input)
results = {**errors, **biases}
_add_supplementary_errors(results, drizzle_indices, error_input)
_add_supplementary_biases(results, bias_input)
return _convert_to_db(results)
def _calc_parameter_errors(drizzle_indices, error_input):
def _calc_dia_error():
error = _calc_error(2 / 7, (1, 1), error_input, add_mu=True)
error_small = _calc_error(1 / 4, (1, 1), error_input, add_mu_small=True)
return _stack_errors(error, drizzle_indices, error_small)
def _calc_lwc_error():
error = _calc_error(1 / 7, (1, 6), error_input)
error_small = _calc_error(1 / 4, (1, 3), error_input)
return _stack_errors(error, drizzle_indices, error_small)
def _calc_lwf_error():
error = _calc_error(1 / 7, (3, 4), error_input, add_mu=True)
error_small = _calc_error(1 / 2, (1, 1), error_input, add_mu_small=True)
error_tiny = _calc_error(1 / 4, (3, 1), error_input, add_mu_small=True)
return _stack_errors(error, drizzle_indices, error_small, error_tiny)
def _calc_s_error():
error = _calc_error(1 / 2, (1, 1), error_input)
return _stack_errors(error, drizzle_indices)
return {'Do_error': _calc_dia_error(),
'drizzle_lwc_error': _calc_lwc_error(),
'drizzle_lwf_error': _calc_lwf_error(),
'S_error': _calc_s_error()}
def _calc_parameter_biases(bias_input):
def _calc_bias(scale, weights):
return utils.l2norm_weighted(bias_input, scale, weights)
dia_bias = _calc_bias(2/7, (1, 1))
lwc_bias = _calc_bias(1/7, (1, 6))
lwf_bias = _calc_bias(1/7, (3, 4))
return {'Do_bias': dia_bias,
'drizzle_lwc_bias': lwc_bias,
'drizzle_lwf_bias': lwf_bias}
def _add_supplementary_errors(results, drizzle_indices, error_input):
def _calc_n_error():
z_error = error_input[0]
dia_error = db2lin(results['Do_error'])
n_error = utils.l2norm(z_error, 6 * dia_error)
return _stack_errors(n_error, drizzle_indices)
def _calc_v_error():
error = results['Do_error']
error[drizzle_indices['tiny']] *= error[drizzle_indices['tiny']]
return error
results['drizzle_N_error'] = _calc_n_error()
results['v_drizzle_error'] = _calc_v_error()
results['mu_error'] = MU_ERROR
return results
def _add_supplementary_biases(results, bias_input):
def _calc_n_bias():
z_bias = bias_input[0]
dia_bias = db2lin(results['Do_bias'])
return utils.l2norm_weighted((z_bias, dia_bias), 1, (1, 6))
results['drizzle_N_bias'] = _calc_n_bias()
results['v_drizzle_bias'] = results['Do_bias']
return results
def _convert_to_db(results):
"""Converts linear error values to dB."""
return {name: lin2db(value) for name, value in results.items()}
def _calc_error(scale, weights, error_input, add_mu=False, add_mu_small=False):
error = utils.l2norm_weighted(error_input, scale, weights)
if add_mu:
error = utils.l2norm(error, MU_ERROR)
if add_mu_small:
error = utils.l2norm(error, MU_ERROR_SMALL)
return error
def _stack_errors(error_in, drizzle_indices, error_small=None, error_tiny=None):
def _add_error_component(source, ind):
error[ind] = source[ind]
error = ma.zeros(error_in.shape)
_add_error_component(error_in, drizzle_indices['drizzle'])
if error_small is not None:
_add_error_component(error_small, drizzle_indices['small'])
if error_tiny is not None:
_add_error_component(error_tiny, drizzle_indices['tiny'])
return error
COR = 10 / np.log(10)
def db2lin(x):
if ma.max(x) > 100:
raise ValueError('Too large values in drizzle.db2lin()')
return ma.exp(x / COR) - 1
def lin2db(x):
if ma.min(x) < -0.9:
raise ValueError('Too small values in drizzle.lin2db()')
return ma.log(x + 1) * COR
| [
"simo.tukiainen@fmi.fi"
] | simo.tukiainen@fmi.fi |
22dfaba28c59c06bab37c8db0df174e75f3bf706 | bd9278423bb215dcdbf9f56a948210db044bdba2 | /tests/test_01_main/test_env_vars_1.py | 501ad06fecc21417b497a0cafa4e66f9cbcc5426 | [
"MIT"
] | permissive | dungnv2602/uvicorn-gunicorn-docker | 77fd5e0d07a94c7acc0876a773e6b1262619fb6d | 37dbc188e555c22cf9b2dd0f3f6ab3e122e32c24 | refs/heads/master | 2020-04-26T16:40:32.749609 | 2019-02-08T10:44:24 | 2019-02-08T10:44:24 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,250 | py | import time
import pytest
import requests
import docker
from ..utils import CONTAINER_NAME, get_config, get_logs, remove_previous_container
client = docker.from_env()
def verify_container(container, response_text):
config_data = get_config(container)
assert config_data["workers_per_core"] == 1
assert config_data["host"] == "0.0.0.0"
assert config_data["port"] == "8000"
assert config_data["loglevel"] == "warning"
assert config_data["bind"] == "0.0.0.0:8000"
logs = get_logs(container)
assert "Checking for script in /app/prestart.sh" in logs
assert "Running script /app/prestart.sh" in logs
assert (
"Running inside /app/prestart.sh, you could add migrations to this file" in logs
)
response = requests.get("http://127.0.0.1:8000")
assert response.text == response_text
@pytest.mark.parametrize(
"image,response_text",
[
(
"tiangolo/uvicorn-gunicorn:python3.6",
"Hello world! From Uvicorn with Gunicorn. Using Python 3.6",
),
(
"tiangolo/uvicorn-gunicorn:python3.7",
"Hello world! From Uvicorn with Gunicorn. Using Python 3.7",
),
(
"tiangolo/uvicorn-gunicorn:latest",
"Hello world! From Uvicorn with Gunicorn. Using Python 3.7",
),
(
"tiangolo/uvicorn-gunicorn:python3.6-alpine3.8",
"Hello world! From Uvicorn with Gunicorn in Alpine. Using Python 3.6",
),
(
"tiangolo/uvicorn-gunicorn:python3.7-alpine3.8",
"Hello world! From Uvicorn with Gunicorn in Alpine. Using Python 3.7",
),
],
)
def test_env_vars_1(image, response_text):
remove_previous_container(client)
container = client.containers.run(
image,
name=CONTAINER_NAME,
environment={"WORKERS_PER_CORE": 1, "PORT": "8000", "LOG_LEVEL": "warning"},
ports={"8000": "8000"},
detach=True,
)
time.sleep(1)
verify_container(container, response_text)
container.stop()
# Test that everything works after restarting too
container.start()
time.sleep(1)
verify_container(container, response_text)
container.stop()
container.remove()
| [
"tiangolo@gmail.com"
] | tiangolo@gmail.com |
dd21ece637d6fcb68d7c8ef62c5fb3fc672fce85 | 20d4e820eb6eda5c9cdac2701855f449a4172973 | /mf5f.py | 6c82f4b480892fb2f5e66f2d647f189c2e45d244 | [] | no_license | ikoblyk/rozrahachysl | fa1d02b5a7c30d3b239e202b7281348abbd9fdf1 | 136c6514fc53129552f6f7457eddcc5467c87e86 | refs/heads/master | 2020-08-29T19:01:55.595896 | 2019-12-13T09:35:36 | 2019-12-13T09:35:36 | 218,139,683 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 178 | py | import matplotlib.pyplot as plt
from rozraha import hlp, x
plt.plot(x, hlp["mf5f"])
plt.title(" Медіанне послідовне згладжування W = 5")
plt.show() | [
"ivankob.16@gmail.com"
] | ivankob.16@gmail.com |
9ff8ce9fa14d395632167561ea0ce130191169e5 | 0c71da2a9d9c3b03aff74c60c9d9f572650ff6ac | /semTracker/exams.py | 6cb5ca4e22b7d92794a2202b3f0cdf5b3648d30e | [] | no_license | ach0309/SemesterTracker | d772a0a099d87be18485679910ac27d340cca866 | ff8b8f953415164588acfae5a5c8681cf0d62bb2 | refs/heads/master | 2022-12-19T00:19:36.807543 | 2020-09-25T02:28:08 | 2020-09-25T02:28:08 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 394 | py | class Exam:
def __init__(self, num, grade):
self.num = num
self.grade = grade
def assignInfo(self):
a_info = "Exam #" + str(self.num) + ": " + str(self.grade) + "%"
return a_info
def __str__(self):
return str(self.grade)
def toPercent(self, score, total):
grade = ( score/total ) * 100
return "{:.2f}".format(grade)
| [
"noreply@github.com"
] | noreply@github.com |
99cd3f6155cd78ae7249f084ee1a75a0331c43b9 | ef04e901ca46c865e8ea9f542429e8512efb3121 | /2020/M3/Transformadas/color.py | bc679e42f0486705c6b381b5ef455aa0ab76456f | [] | no_license | osoriodiego-utp/UTP-Grafica | 201790c76e26051da02713a41ae95cc12ca63ce9 | c26c99b971d12418809f0976f6b02464cecc9765 | refs/heads/master | 2023-01-24T04:47:27.367899 | 2020-12-08T06:16:44 | 2020-12-08T06:16:44 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,658 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# https://www.cerotec.net/tabla-colores-web/
#
# To use: from color import *
class color(object):
#ROJOS
rojo_indio = 205, 92, 205
cordal_suave = 240, 128, 128
salmon = 250, 128, 114
salmon_oscuro = 233, 150, 122
salmon_suave = 255, 160, 122
carmesi = 220, 20, 60
rojo = 255, 0, 0
rojo_fuego = 178, 34, 34
rojo_oscuro = 139, 0, 0
#ROSAS
rosa = 255, 192, 203
rosa_suave = 255, 182, 193
rosa_calido = 255, 105, 180
rosa_profundo = 255, 20, 147
rosa_violeta = 199, 21, 133
rosa_pastel = 219, 112, 147
#NARANJAS
salmon_suave = 255, 160, 122
naranja_coral = 255, 127, 80
tomate = 255, 99, 71
naranja_rojizo = 255, 69, 0
naranja_oscuro = 255, 140, 0
naranja_puro = 255, 165, 0
#AMARILLOS
amarillo_oro = 255, 215, 0
amarillo = 255, 255, 0
amarillo_suave = 255, 255, 224
amarillo_limon = 255, 250, 205
amarillo_manzana = 250, 250, 210
#PURPURAS
espliego = 230, 230, 250
cardo = 216, 191, 216
ciruela = 221, 160, 221
violeta = 238, 130, 238
violeta_oscuro = 148, 0, 211
magenta = 255, 0, 255
magenta_oscuro = 139, 0, 139
amatista = 153, 102, 204
azul_violeta = 138, 43, 226
purpura = 128, 0, 128
purpura_medio = 147, 112, 219
orquidea = 218, 112, 214
orquidea_medio = 186, 85, 211
orquidea_oscuro = 153, 50, 204
indigo = 75, 0, 130
azul_pizarra = 106, 90, 205
azul_pizarra_medio = 123, 104, 238
azul_pizarra_oscuro = 72, 61, 139
#VERDES
verde_amarillo = 173, 255, 47
verde_cartujano = 127, 255, 0
verde_cesped = 124, 253, 0
lima = 0, 255, 0
verde_lima = 50, 205, 50
verde_palido = 152, 251, 152
verde_claro = 144, 238, 144
verde_primavera = 0, 255, 127
verde_primavera_medio = 0, 250, 154
verde_mar= 46, 139, 87
verde_mar_claro = 32, 178, 170
verde_mar_medio = 60, 179, 113
verde_mar_oscuro = 143, 188, 143
verde_bosque = 34, 139, 34
verde = 0, 128, 0
verde_oscuro = 0, 100, 0
amarillo_verdoso = 154, 205, 50
oliva = 128, 128, 0
verde_oliva = 107, 142, 35
verde_oliva_oscuro = 85, 107, 47
aguamarina_medio = 102, 205, 170
cyan_oscuro = 0, 139, 139
carcel = 0, 128, 128
#AZULES
#MARRONES
#BLANCOS
blanco = 255, 255, 255
blanco_nieve = 255, 250, 250
miel_crema = 240, 255, 240
menta_crema = 245, 255, 250
azul_celeste = 240, 255, 255
azul_alicia = 240, 248, 255
blanco_fantasma = 248, 248, 255
blanco_humo = 245, 245, 245
concha = 255, 245, 238
beige = 245, 245, 220
blanco_conrdon = 253, 245, 230
blanco_floral = 255, 250, 240
blanco_marfil = 255, 255, 240
marfil = 255, 255, 240
blanco_antiguo = 250, 235, 215
blanco_lino = 250, 240, 230
lavanda = 255, 240, 245
palo_de_rosa = 255, 228, 225
#NEGRO
negro = 0, 0, 0
| [
"dieferosorio@utp.edu.co"
] | dieferosorio@utp.edu.co |
5464413b2090c582e1fca7a4a5a3f513bfdd7f6e | 1f4e17c2cc2ef2cbb74bb8b5ce7dd93933529e0a | /script.py | d9ee6fd47f0dcd7b07c8162c20571b9d533f881f | [] | no_license | rohitkumar003/reader-mediacenterjs | cde5bfcc1550559b67ebdd46290fff0c93376e6d | eb2117d4c5bbf02940eb9c0df10f741661b4a126 | refs/heads/master | 2021-05-16T03:02:26.800532 | 2019-03-04T12:09:20 | 2019-03-04T12:09:20 | 42,649,516 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,976 | py | import subprocess
import sys
import re
import os
import zipfile
import time
import datetime
import shutil
# check if wiki folder exists, if yes then delete existing folder
_MAX_COUNT=5
_GREETING ="Welcome,\n"
_folder = "reader-mediacenterjs.wiki"
_commit = str(subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD']))
_branch = subprocess.check_output(['git','branch','-r']).split('\n')[1].split('/')[1]
print 'branch '+_branch
filename = (_branch+"-"+_commit)[:-1]
if os.path.exists(_folder):
subprocess.call(['chmod', '-R', '+w', _folder])
shutil.rmtree(_folder)
print "folder removed"
# clone the wiki dir
subprocess.call(["git","clone","https://github.com/rohitkumar003/reader-mediacenterjs.wiki.git"]) # Cloning
print "clone successful"
os.chdir("./reader-mediacenterjs.wiki/")
print "changed dir"
if(not os.path.isfile(filename+'.zip')):
shutil.make_archive(filename, 'zip', '../build/debug')
print "file zipped"
else:
print exit
artifacts = []
with open('testpage.md', 'r') as f:
artifacts = f.readlines()
artifacts = filter(lambda x: x.find('.zip')!=-1, artifacts)
if(len(artifacts)== _MAX_COUNT):
searchObj = re.search( r'[\(](.*)[\)]', artifacts[0], re.M|re.I)
if searchObj:
file_to_be_removed = searchObj.group(1)
subprocess.call(['git','rm',file_to_be_removed])
print 'Artifact removed: '+ file_to_be_removed
else:
print 'No Artifact found to remove'
artifacts = [x.strip('\n') for x in artifacts][0:_MAX_COUNT]
artifacts.append('['+filename+'] ('+filename+'.zip)')
print 'contents '+str(artifacts)
with open('testpage.md', 'w+') as f:
f.write('\n'.join(artifacts))
f.close()
print subprocess.check_output(['git', 'status','-s'])
subprocess.call(["git","add","{0}.zip".format(filename)])
subprocess.call(["git","add","testpage.md"])
subprocess.call(["git","commit","-m"," 'Uploading artifacts'"])
print "changes commit"
subprocess.call(["git","push"])
os.chdir("../")
| [
"rohit.kumar.003@gmail.com"
] | rohit.kumar.003@gmail.com |
a2fff788a3bf339242af472e231ea2e64b740a53 | adea9fc9697f5201f4cb215571025b0493e96b25 | /napalm_yang/models/openconfig/network_instances/network_instance/protocols/protocol/ospfv2/areas/__init__.py | ec0be8d4e67f577dc03415c82b56d94adae2a930 | [
"Apache-2.0"
] | permissive | andyjsharp/napalm-yang | d8a8b51896ef7c6490f011fe265db46f63f54248 | ef80ebbfb50e188f09486380c88b058db673c896 | refs/heads/develop | 2021-09-09T02:09:36.151629 | 2018-03-08T22:44:04 | 2018-03-08T22:44:04 | 114,273,455 | 0 | 0 | null | 2018-03-08T22:44:05 | 2017-12-14T16:33:35 | Python | UTF-8 | Python | false | false | 10,450 | py |
from operator import attrgetter
from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType
from pyangbind.lib.yangtypes import RestrictedClassType
from pyangbind.lib.yangtypes import TypedListType
from pyangbind.lib.yangtypes import YANGBool
from pyangbind.lib.yangtypes import YANGListType
from pyangbind.lib.yangtypes import YANGDynClass
from pyangbind.lib.yangtypes import ReferenceType
from pyangbind.lib.base import PybindBase
from decimal import Decimal
from bitarray import bitarray
import six
# PY3 support of some PY2 keywords (needs improved)
if six.PY3:
import builtins as __builtin__
long = int
unicode = str
elif six.PY2:
import __builtin__
from . import area
class areas(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module openconfig-network-instance - based on the path /network-instances/network-instance/protocols/protocol/ospfv2/areas. Each member element of
the container is represented as a class variable - with a specific
YANG type.
YANG Description: Configuration and operational state relating to an
OSPFv2 area.
"""
__slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_extmethods', '__area',)
_yang_name = 'areas'
_pybind_generated_by = 'container'
def __init__(self, *args, **kwargs):
self._path_helper = False
self._extmethods = False
self.__area = YANGDynClass(base=YANGListType("identifier",area.area, yang_name="area", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='identifier', extensions=None), is_container='list', yang_name="area", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='list', is_config=True)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path()+[self._yang_name]
else:
return [u'network-instances', u'network-instance', u'protocols', u'protocol', u'ospfv2', u'areas']
def _get_area(self):
"""
Getter method for area, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area (list)
YANG Description: The OSPFv2 areas within which the local system exists
"""
return self.__area
def _set_area(self, v, load=False):
"""
Setter method for area, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area (list)
If this variable is read-only (config: false) in the
source YANG file, then _set_area is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_area() directly.
YANG Description: The OSPFv2 areas within which the local system exists
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=YANGListType("identifier",area.area, yang_name="area", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='identifier', extensions=None), is_container='list', yang_name="area", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='list', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """area must be of a type compatible with list""",
'defined-type': "list",
'generated-type': """YANGDynClass(base=YANGListType("identifier",area.area, yang_name="area", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='identifier', extensions=None), is_container='list', yang_name="area", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='list', is_config=True)""",
})
self.__area = t
if hasattr(self, '_set'):
self._set()
def _unset_area(self):
self.__area = YANGDynClass(base=YANGListType("identifier",area.area, yang_name="area", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='identifier', extensions=None), is_container='list', yang_name="area", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='list', is_config=True)
area = __builtin__.property(_get_area, _set_area)
_pyangbind_elements = {'area': area, }
from . import area
class areas(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module openconfig-network-instance-l2 - based on the path /network-instances/network-instance/protocols/protocol/ospfv2/areas. Each member element of
the container is represented as a class variable - with a specific
YANG type.
YANG Description: Configuration and operational state relating to an
OSPFv2 area.
"""
__slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_extmethods', '__area',)
_yang_name = 'areas'
_pybind_generated_by = 'container'
def __init__(self, *args, **kwargs):
self._path_helper = False
self._extmethods = False
self.__area = YANGDynClass(base=YANGListType("identifier",area.area, yang_name="area", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='identifier', extensions=None), is_container='list', yang_name="area", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='list', is_config=True)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path()+[self._yang_name]
else:
return [u'network-instances', u'network-instance', u'protocols', u'protocol', u'ospfv2', u'areas']
def _get_area(self):
"""
Getter method for area, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area (list)
YANG Description: The OSPFv2 areas within which the local system exists
"""
return self.__area
def _set_area(self, v, load=False):
"""
Setter method for area, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area (list)
If this variable is read-only (config: false) in the
source YANG file, then _set_area is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_area() directly.
YANG Description: The OSPFv2 areas within which the local system exists
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=YANGListType("identifier",area.area, yang_name="area", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='identifier', extensions=None), is_container='list', yang_name="area", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='list', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """area must be of a type compatible with list""",
'defined-type': "list",
'generated-type': """YANGDynClass(base=YANGListType("identifier",area.area, yang_name="area", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='identifier', extensions=None), is_container='list', yang_name="area", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='list', is_config=True)""",
})
self.__area = t
if hasattr(self, '_set'):
self._set()
def _unset_area(self):
self.__area = YANGDynClass(base=YANGListType("identifier",area.area, yang_name="area", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='identifier', extensions=None), is_container='list', yang_name="area", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='list', is_config=True)
area = __builtin__.property(_get_area, _set_area)
_pyangbind_elements = {'area': area, }
| [
"dbarrosop@dravetech.com"
] | dbarrosop@dravetech.com |
57eede34af20d7d21322bba65367464d13a27175 | 1ed3a78f37d97d6b3fdfbe1777190c1e94d7d967 | /2-Arrays/CyclicRotation.py | d5ee1604be937e5f7cefc8ad7ac16a80953f1e1c | [] | no_license | tarsioonofrio/codility | 154f4711abc52a865f0e5167d2bb4e5675da0ecd | 51aa3e937715dcab6658ba774a5690dc5ffa0539 | refs/heads/master | 2021-05-20T11:57:11.692568 | 2020-04-05T20:15:01 | 2020-04-05T20:15:01 | 252,285,380 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 195 | py | # you can write to stdout for debugging purposes, e.g.
# print("this is a debug message")
from collections import deque
def solution(A, K):
d = deque(A)
d.rotate(K)
return list(d)
| [
"5504075+tarsioonofrio@users.noreply.github.com"
] | 5504075+tarsioonofrio@users.noreply.github.com |
0ad1426d1e43e8323cd4178c8c0dcebb7e9b9290 | fa38b704f9faf3889a4a35c6edce82be2e9b0274 | /Practice_Query/apps.py | be35856f927e72567c0382dbb335f702d2f40ebb | [] | no_license | Efrana/Django_Practice | 4a4bc0f5016eb6fda94f5184a1bfbf7cc6db757b | 864e4cb80b90b59deeeb7fd9a30f8af82d58658a | refs/heads/master | 2022-11-22T00:30:11.809382 | 2020-06-08T18:13:31 | 2020-06-08T18:13:31 | 270,774,361 | 0 | 1 | null | 2020-07-19T20:01:32 | 2020-06-08T18:09:45 | Python | UTF-8 | Python | false | false | 102 | py | from django.apps import AppConfig
class PracticeQueryConfig(AppConfig):
name = 'Practice_Query'
| [
"efrana.jannat@gmail.com"
] | efrana.jannat@gmail.com |
549ad18aa00c73644e90458a3ec9cbf3fd9e36bb | e34112cf8e4cfccde629a1adec2be4ad2813063a | /while_practice.py | 47c2c1cb8e30b205a090e30df78932de445addf3 | [] | no_license | Limitlessmatrix/automation_scripts | ff64476108651b111a4dbc96207641702c7c8bae | a74c86f570629e4b680413f0ea6fc0916b51a880 | refs/heads/master | 2023-01-24T05:18:04.468908 | 2020-12-07T23:56:39 | 2020-12-07T23:56:39 | 309,514,041 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 754 | py | #This will be a practice of while loops trying to use them in real world problems.
#
def average(number_1, number_2, total):
return ((int(number_1) + int(number_2)) / total)
average(2, 4543, 2)
#better way to get averages
#take into consideration lists of numbers, and dictionaries
#Any other common data types you might be messing with
loop_1 = 1
while (loop_1 <= 23):
print(loop_1)
loop_1= loop_1 + 1
# while loop in a function
#def run_commands():
# your_input = 29
# your_input = your_input + 1
# print(your_input)
#while(your_input <=70):
# run_commands()
#google crash course examples
#
def attempts(n):
x = 1
while x <= n:
print("Attempt " + str(x))
x += 1
print("Done")
attempts(5) | [
"28797311+Limitlessmatrix@users.noreply.github.com"
] | 28797311+Limitlessmatrix@users.noreply.github.com |
c2302fb7a4bd45a0e4b8917ab1a33fee00843b50 | 3d4f77a5c849e3eeeee311982956353890bcdb85 | /modules/utilities.py | 7b60179f8b21afecb18beab58e3e7c7c1f825bf4 | [] | no_license | symbolix/unimogFlags | 7c789dc60c92ea70819b80ce0571dc374312dfd0 | c4e23079912c8c2daec6642b79b452d8b5cbaee3 | refs/heads/master | 2022-11-14T12:07:54.997418 | 2020-07-03T13:03:20 | 2020-07-03T13:03:20 | 276,893,562 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 10,748 | py | #!/usr/bin/env python2.7
# ;-----------------------------------------------------------------------------------------------
# ; {UNIMOG} Integrated Pipeline Tools
# ;
# ; Name : pathconfig.utilities
# ; Author : Muhittin Bilginer
# ; Created : 15/06/2014
# ;
# ; Info : Main functions and classes used by the unimogDev.py tool.
# ;
# ; This tool is part of Unimog.
# ;-----------------------------------------------------------------------------------------------
# Declare external imports
import sys
import os
import re
import time
import string
import logging
import pprint
from itertools import imap
# Define a version variable:
moduleName = __name__
moduleVersion = moduleName + " 0.0.1.[6]"
# Set a local empty logger to avoid the "No handlers could be found for logger FOO"
# message in case logging is not set up properly up the chain of the parent application.
logging.getLogger('unimog.unimogdev.utilities').addHandler(logging.NullHandler())
# Initialise YAML {{{
logger = logging.getLogger('unimog.unimogdev.utilities')
try:
from yaml import load, dump
try:
from yaml import CLoader as Loader, CDumper as Dumper
logger.debug("%s" % ("C module LibYAML is available."))
except ImportError:
try:
from yaml import Loader, Dumper
except:
logger.warning("%s" % ("C module LibYAML is NOT available."))
except:
logger.critical("YAML module is not available.")
logger.critical("EXIT_CODE: 1")
sys.exit(1)
# }}}
# Setup the Utility Functions {{{
# UTILITY: Define a sample function {{{
# ;---------------------------------------------------------------------------
# ; A test function
# ;---------------------------------------------------------------------------
def printMessage():
logger = logging.getLogger('unimog.unimogdev.utilities')
logger.debug("External module accessed.")
#}}}
# UTILITY: Define a KEY extractor function {{{
# ;---------------------------------------------------------------------------
# ; The "extractKey" Function:
# ; This function should return a list of dictionary keys.
# ;---------------------------------------------------------------------------
def extractKeys(dictionary):
keyList = []
for key in dictionary:
keyList.append(key)
return keyList
#}}}
# Any additional function goes here.
#}}}
# Setup the YAML Functions {{{
# YAML: Define an "Importer" function {{{
# ;---------------------------------------------------------------------------
# ; Import Yaml Data:
# ; Accepts the incoming YAML data through the stream object
# ; passed in as a function argument.
# ;---------------------------------------------------------------------------
def importYamlData(fileName):
# Link to logger
logger = logging.getLogger('unimog.unimogdev.utilities')
# Create the stream object
try:
inStream = open(fileName, 'r')
except:
logger.critical("%s: %s" % ("Unable to access the input file handler", fileName))
logger.critical("EXIT_CODE: 2")
sys.exit(2)
# Perform the import process
try:
incomingData = load(inStream, Loader=Loader)
logger.debug("%s" % ("Configuration successfully imported through the file handler."))
inStream.close()
return incomingData
except:
logger.critical("Failed to import the configuration through the file handler!")
return {'error':'broken data handler'}
# }}}
# YAML: Define an "Exporter" function {{{
# ;---------------------------------------------------------------------------
# ; Export Yaml Data:
# ; Dumps out an incoming dictionary using the stream object
# ; passed in with the function arguments.
# ;---------------------------------------------------------------------------
def exportYamlData(sourceDictionary, fileName):
# Link to logger
logger = logging.getLogger('unimog.unimogdev.utilities')
# Run a file check
if not os.path.isfile(fileName):
logger.critical("%s: %s" % ("Unable to access the output file handler", fileName))
logger.critical("EXIT_CODE: 3")
sys.exit(3)
# Create the stream object
outStream = open(fileName, 'w')
# Perform the import process
try:
output = dump(sourceDictionary, outStream, Dumper=Dumper, default_flow_style=False)
logger.debug("%s" % ("Configuration successfully exported to the file handler."))
except:
logger.critical("Failed to export the configuration to file handler!")
# }}}
# Any additional function goes here.
#}}}
# Setup Main Functions {{{
# ;---------------------------------------------------------------------------
# ; Setup the core functions
# ;---------------------------------------------------------------------------
# OPERATION: Define an "Get" function {{{
# ;---------------------------------------------------------------------------
# ; The "executeGet" Function:
# ; This is one of the main operational functions. The role of this
# ; function is to get the flags for the "targetObject".
# ;---------------------------------------------------------------------------
def executeGet(yamlObject, devVar):
# Link to logger
logger = logging.getLogger('unimog.unimogdev.utilities')
logger.debug("%s: %s\n" % ("<get> call for", yamlObject))
try:
print ("1" if getattr(yamlObject, devVar[0]) else "0")
logger.debug("\"%s\" %s" % (devVar[0], "is a valid dev variable."))
except:
logger.critical("\"%s\" %s" % (devVar[0], "is NOT a valid dev variable!"))
#}}}
# OPERATION: Define the "Set / Unset" function {{{
# ;---------------------------------------------------------------------------
# ; The "executeSet" Function:
# ; This is one of the main operational functions. The role of this
# ; function is to update the flags for the "targetObjects" to TRUE or FALSE.
# ;---------------------------------------------------------------------------
def executeSet(yamlObject, devVar, verbosityFlag, state):
# This is a hybrid set / unset function, so handle the verbose string
# Link to logger
logger = logging.getLogger('unimog.unimogdev.utilities')
stateSignature = "unset"
if state: stateSignature = "set"
# Link debugger
logger.debug("<%s> %s: %s\n" % (stateSignature, "call for", yamlObject))
logger = logging.getLogger('unimog.unimogdev.utilities')
# Cycle over the provided variables
# Create a counter
index = 0
# Start iterator
for variable in devVar:
# Debug information
if verbosityFlag > 2: print "%s%s%s" % ("Variable Cycle [", index, "]")
# Check if the provided value is a key in our YAML object
# If it is, update the flag
if variable in yamlObject.GetPublicDict():
if verbosityFlag > 2: print "\t%s%s%s{%s}" % ("Current value for [", variable, "] ", getattr(yamlObject, variable))
setattr(yamlObject, variable, state)
if verbosityFlag > 2: print "\t%s%s%s{%s}" % ("New value for [", variable, "] ", getattr(yamlObject, variable))
# If NOT, skip the process
else:
if verbosityFlag > 2: print "\t%s%s%s" % ("Variable [", variable, "] does NOT exist.")
if verbosityFlag > 2: print "\t%s" % ("Nothing to process.")
# Update the counter
index = index + 1
# Return the modified YAML object
return yamlObject.GetPublicDict()
#}}}
# OPERATION: Define a "List" function {{{
# ;---------------------------------------------------------------------------
# ; The "executeList" Function:
# ; This is one of the main operational functions. The role of this
# ; function is to list the flags for the "targetObject(s)".
# ;---------------------------------------------------------------------------
def executeList(yamlObject, mode="default"):
# Link debugger
logger = logging.getLogger('unimog.unimogdev.utilities')
logger.debug("%s: %s\n" % ("<list> call for", yamlObject))
if mode == "default":
# Print the results
print ""
yamlObject.listElements()
print ""
elif mode == "bash":
pythonDict = yamlObject.GetPublicDict()
bashString = ' '.join('{}={}'.format(key, int(val)) for key, val in pythonDict.items())
print bashString
elif mode == "python":
pythonDict = yamlObject.GetPublicDict()
print pythonDict
else:
pass
#}}}
# Any additional function goes here.
#}}}
# Setup the Core Functions {{{
# CORE: Define an "Empty Template" function {{{
# ;---------------------------------------------------------------------------
# ; Empty template:
# ; Information goes here.
# ;---------------------------------------------------------------------------
# Any additional functions go here.
#}}}
# Setup Object Classes {{{
# Setup a template object {{{
# ;---------------------------------------------------------------------------
# ; Template Object
# ;---------------------------------------------------------------------------
class YamlObj:
def __init__(self, **entries):
self.__dict__.update(entries)
self.public_names = entries.keys()
def testMe(self):
self.myVariable = 67
def GetPublicDict(self):
return {key:getattr(self, key) for key in self.public_names}
def GetBashDict(self):
return 0
def listElements(self):
#return str(self.GetPublicDict())
self.maxKeyLength = max(imap(len, self.GetPublicDict()))
for key, value in self.GetPublicDict().items():
print "{0:{width}} : [{1:5}]".format(key, str(value), width=self.maxKeyLength)
#}}}
# Setup a custom StreamHandler object {{{
# ;---------------------------------------------------------------------------
# ; Conditional Handler Filter
# ;---------------------------------------------------------------------------
class CustomFilter(logging.Filter):
def __init__(self, state):
self.state = state
def filter(self, record):
if self.state == 1:
return record.levelno in [logging.INFO, logging.ERROR,
logging.CRITICAL]
elif self.state == 2:
return record.levelno in [logging.INFO, logging.DEBUG,
logging.ERROR, logging.CRITICAL]
elif self.state > 2:
return record.levelno in [logging.INFO, logging.DEBUG,
logging.ERROR, logging.CRITICAL,
logging.WARNING]
else:
return False
#}}}
# Any additional class goes here.
#}}}
# vim: ts=4 ft=python nowrap fdm=marker
| [
"muhittin.bilginer@gmail.com"
] | muhittin.bilginer@gmail.com |
5376d99a9743087c70df34ecdf6874d4028d72f6 | 9309b05ab53e89d888ee66d9a4225b6677b70fcf | /myPackage/test_Login.py | 5e03efdcbcad188dbfb4bac2ea918b5aab5fe045 | [] | no_license | jmonfu/python-selenium | 2109a1249f35ed37ee5e44d118b7407b0b26d0d4 | 4c939a6b557902a245598875d20e2bad823666a5 | refs/heads/master | 2022-06-05T08:12:00.070585 | 2020-05-05T11:10:28 | 2020-05-05T11:10:28 | 254,823,091 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 410 | py | import pytest
@pytest.yield_fixture
def setup():
print("Open URL to loging")
yield
print("Close brower window after login")
def test_LoginByEmail(setup): # we have to pass the testFixture ("setup") for it to run before
print("this is login by Email")
def test_LoginByFacebook(setup): # we have to pass the testFixture ("setup") for it to run before
print("this is login by Facebook")
| [
"jmonfu@gmail.com"
] | jmonfu@gmail.com |
03e810adfd8d18831b646c8bd14755a5cc94b5e4 | 05a1e42b39adfc6f7362a0f184c78694f62c87e8 | /Lib/Lib_mk.py | d41c008dae0f4bfc54231a749d7b6b0270905a1c | [] | no_license | ericsonj/stm32f4disco | 057bcddb7e0efdc1e31f12fcfdae9bd808070ed1 | 6f662b9e92a15a06d3c5e5af689da42734102bba | refs/heads/master | 2021-02-11T05:24:03.691386 | 2020-03-19T19:10:31 | 2020-03-19T19:10:31 | 244,458,918 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 140 | py | from pybuild import preutil
def getSrcs(wk):
return preutil.getAllSrcs_C(wk)
def getIncs(wk):
return preutil.getAllIncs_C(wk)
| [
"eric.surix@gmail.com"
] | eric.surix@gmail.com |
28ceb33af35360d9b276b8efca25afef25de25cf | d7b2990f8021de9fbd58067b3fc55073d56bd2e8 | /dbscriptcmd.py | 7dbb631ee6e1ca6a1c273c0d910c9a59fff9ea27 | [] | no_license | Harrywekesa/Sqlite-database | a941a740ff10451efee7eb2ee11ee7d724b7356d | 05d63d54e86afb25b30732066956148b0e8365c3 | refs/heads/master | 2023-06-15T01:26:43.150021 | 2021-07-09T06:51:12 | 2021-07-09T06:51:12 | 384,018,137 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 409 | py | import sqlite3
with sqlite3.connect("script.db") as conn:
c = conn.cursor()
c.executescript(""" DROP TABLE IF EXISTS people;
CREATE TABLE people(First_name TEXT, Last_name TEXT, Age INT);
INSERT INTO people VALUES ('Harrison', 'Wekesa',22);
""")
#Executescript()/ executemany() is used when running more than one line of SQL code at a time | [
"harrisonwekesa09@gmail.com"
] | harrisonwekesa09@gmail.com |
3462968cf9b4b4edf17691f19caaff12ffe80c11 | a0227e40844d6bbe49b7a07abcc86d365726a2d2 | /HARD_WAY/ex21.py | f3f48149f296838750e11cc24d71e3aa1c2ae9f1 | [] | no_license | vicwuht/pl-hd | d32364f9938890ecd45d784b974331a2c571c326 | e7ada01ddd18ff6364e26a4adafc2afa78a49b82 | refs/heads/master | 2021-06-05T12:28:51.803561 | 2020-05-08T15:38:19 | 2020-05-08T15:38:19 | 146,396,843 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 644 | py | def add(a, b):
print(f"ADDING {a} + {b}")
return a + b
def subtract(a, b):
print(f"SUBTRACTING {a} - {b}")
return a - b
def multiply(a, b):
print(f"MULTIPLYING {a} * {b}")
return a * b
def divide(a, b):
print(f"DIVIDING {a} / {b}")
return a / b
print("Let's do some math with just functions!")
age = add(30, 5)
height = subtract(78, 4)
weight = multiply(90, 2)
iq = divide(100, 2)
print(f"Age: {age}, Height:{height}, Weight:{multiply}, IQ:{iq}")
print("Here is a puzzle.")
what = add(age, subtract(height, multiply(weight, divide(iq, 2))))
print("That becomes:",what,"Can you do it by hand?")
| [
"w80u10@163.com"
] | w80u10@163.com |
b59003f3e2b5df92d5bab1bb713ce93a09de5ad7 | 2d010b06a2782d4fdf049fa77d6013dd2dae74be | /test.py | 601482f12f374e04129cb76d08a49b1ec01d9403 | [] | no_license | iscc7/Sound-Shaper | bba88d722e160e07f574c3addf9dbc4a71fb690c | abe6ebf6da653e2410e3dd41f511492719fafb59 | refs/heads/main | 2023-08-28T01:32:28.617605 | 2021-11-09T05:26:40 | 2021-11-09T05:26:40 | 421,006,044 | 0 | 0 | null | 2021-11-07T06:41:51 | 2021-10-25T12:02:23 | Python | UTF-8 | Python | false | false | 576 | py | from B_Spline import BSpline
from point import PointData
from matplotlib import pyplot as plt
from mysound import sound
pp = PointData()
pp.setStart(1.0)
pp.setEnd(2.0)
pp.setAnchors(0, 0.2, 2)
pp.setAnchors(1, 0.3, -1)
pp.setAnchors(2, 0.35, 6)
pp.setAnchors(3, 0.5, 4)
pp.setAnchors(4, 0.6, 0)
pp.insert(0.9, -10)
tmp = pp.getData()
line = BSpline().calc(tmp, sample=100)
x = tmp[:, 0]
y = tmp[:, 1]
sd = sound()
print(sd.soundGen(line, Fstimes=100, Durtime=1))
sd.play()
plt.plot(line[0], line[1])
plt.plot(x, y, '--', marker='o')
plt.show()
| [
"noreply@github.com"
] | noreply@github.com |
63bb40b826c619b0273e46af14ccebeecc73b328 | 87b910161ac0cdfec9897c10a29c561be63d6b09 | /venv/Scripts/pip3.7-script.py | 9067f2a65ad64a1726c591235ebe70495a9d093d | [] | no_license | MISO4204-201910/SISRED-Backend | b34442e63f274b80adf709e539b73137bbcc3915 | c8c2a4c30a1182ca3402e353487acc218425453b | refs/heads/master | 2020-05-19T02:54:22.012925 | 2019-04-26T01:41:06 | 2019-04-26T01:41:06 | 184,790,022 | 0 | 1 | null | 2019-05-03T16:48:42 | 2019-05-03T16:48:41 | null | UTF-8 | Python | false | false | 424 | py | #!C:\Users\usuario\proyecturgent\SISRED-Backend\venv\Scripts\python.exe
# EASY-INSTALL-ENTRY-SCRIPT: 'pip==10.0.1','console_scripts','pip3.7'
__requires__ = 'pip==10.0.1'
import re
import sys
from pkg_resources import load_entry_point
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(
load_entry_point('pip==10.0.1', 'console_scripts', 'pip3.7')()
)
| [
"jsalas2345@hotmail.com"
] | jsalas2345@hotmail.com |
2361663e1f1b4688bb3223866e011d1c585c2c09 | e62c5b4870d492efe8bd519fda0cc75c5bb0468a | /balance.py | 5eb2bfdd16ad2f6b54a0f0ac70252a21f63b74a8 | [] | no_license | coyote963/bmserver | 0298ead4e333cb4ba29e854d20f48307af8f2db1 | ab7e803c1aa18290ab2f47f74f91886448d50f56 | refs/heads/master | 2020-12-30T18:02:06.413282 | 2018-11-28T00:48:11 | 2018-11-28T00:48:11 | 90,938,998 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,435 | py | from collections import deque
from matchupdb import read_and_write
from databaseaccesslayer import find_player, player_rating, add_player, matchupdate
def request_balance(packet,socket, **kwargs):
if packet['option'] == "Chat":
if "!balance" in packet['content']:
conn = kwargs['dbcursor']
bmstream = kwargs['bms']
packet['content'] = "has joined"
is_balanced(packet,socket,**kwargs)
def is_balanced(packet, socket, **kwargs):
if packet['option'] == "Chat":
if "has joined" in packet['content']:
conn = kwargs['dbcursor']
bmstream = kwargs['bms']
print "someone has joined"
cur = conn.cursor()
uscteamroster = deque()
manteamroster = deque()
uscteamratings = []
manteamratings = []
socket.send('/scoreboard\n')
bmstream.read()
manaverage = 0
uscaverage = 0
while bmstream.isEmpty():
bmdict = bmstream.pop()
if bmdict['option'] == "Scoreboard" :
print bmdict
if bmdict['team'] == '1':
uscteamroster.append(bmdict['name'])
elif bmdict['team'] == '2':
manteamroster.append(bmdict['name'])
uscteamsize = len(uscteamroster)
if uscteamsize > 0:
while uscteamroster:
message = '/steam '+ uscteamroster.pop() + '\n'
socket.send(bytes(message))
bmstream.read()
steamdict = bmstream.pop()
if steamdict['option'] != 'Steam ID':
uscaverage = uscaverage + 1000
else:
uscaverage = uscaverage + player_rating(find_player(steamdict['player'],steamdict['steamid'], cur), cur)
uscaverage = uscaverage/uscteamsize
manteamsize = len(manteamroster)
if manteamsize > 0:
while manteamroster:
message = '/steam '+ manteamroster.pop() + '\n'
socket.send(bytes(message))
bmstream.read()
steamdict = bmstream.pop()
if steamdict['option'] != 'Steam ID':
manaverage = manaverage + 1000
else:
manaverage = manaverage + player_rating(find_player(steamdict['player'],steamdict['steamid'], cur), cur)
manaverage = manaverage/manteamsize
if abs(manaverage - uscaverage) > 75:
socket.send(bytes("Teams are very unbalanced\n"))
r1 = 10**(float(manaverage)/400)
r2 = 10**(float(uscaverage)/400)
e1 = r1 / (r1 + r2)
e2 = r2 / (r1 + r2)
socket.send(bytes("Man" + str(int(e1* 100))+ "% USC "+ str(int(e2 * 100)) + '%\n'))
socket.send(bytes("man "+ str( int(manaverage)) + " usc " + str(int(uscaverage))+ '\n')) | [
"coyoteandbird@gmail.com"
] | coyoteandbird@gmail.com |
1602340190e28cb47ee3c4a8aa11ec9b668431a0 | ed5a082d977aefcecc8c40c76046d26334615a8e | /contest/abc/abc147/a.py | a0322ac34844aa2ebbc0d19eb0d875a19fe8e7e9 | [] | no_license | arakoma/competitive_programming | 0ff9b9a97d2f37a3a1dac96c157f3235dde96b85 | ebbc5621860aca320a6949433f1707f1cbfcf911 | refs/heads/master | 2021-08-07T10:50:08.890353 | 2021-07-10T14:10:15 | 2021-07-10T14:10:15 | 223,712,828 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 97 | py | a = list(map(int, input().split()))
if sum(a) >= 22:
print("bust")
else:
print("win") | [
"arakoma555@gmail.com"
] | arakoma555@gmail.com |
8974aa621afe23e3e269301bbe46c506e89b65d6 | cca08359eb4f5f3528ee593c0aaf151226a4e070 | /model.py | 3cdbac65b1b48110f92f6031b9cf50cbd7258e03 | [] | no_license | indussky8/LisGAN | 16e7cdac6ac3eb1aea070a101fef3895e45bd896 | 54d9e250a922936ec88a6e272500c706a8796353 | refs/heads/master | 2020-05-25T17:13:34.137957 | 2019-05-24T17:52:56 | 2019-05-24T17:52:56 | 187,904,609 | 1 | 0 | null | 2019-05-21T19:56:49 | 2019-05-21T19:56:48 | null | UTF-8 | Python | false | false | 9,344 | py | import torch.nn as nn
import torch
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
m.weight.data.normal_(0.0, 0.02)
m.bias.data.fill_(0)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
class MLP_AC_D(nn.Module):
def __init__(self, opt):
super(MLP_AC_D, self).__init__()
self.fc1 = nn.Linear(opt.resSize, opt.ndh)
self.disc_linear = nn.Linear(opt.ndh, 1)
self.aux_linear = nn.Linear(opt.ndh, opt.attSize)
self.lrelu = nn.LeakyReLU(0.2, True)
self.sigmoid = nn.Sigmoid()
self.apply(weights_init)
def forward(self, x):
h = self.lrelu(self.fc1(x))
s = self.sigmoid(self.disc_linear(h))
a = self.aux_linear(h)
return s,a
class MLP_AC_2HL_D(nn.Module):
def __init__(self, opt):
super(MLP_AC_2HL_D, self).__init__()
self.fc1 = nn.Linear(opt.resSize, opt.ndh)
self.fc2 = nn.Linear(opt.ndh, opt.ndh)
self.disc_linear = nn.Linear(opt.ndh, 1)
self.aux_linear = nn.Linear(opt.ndh, opt.attSize)
self.lrelu = nn.LeakyReLU(0.2, True)
self.sigmoid = nn.Sigmoid()
self.dropout = nn.Dropout(p=0.5)
self.apply(weights_init)
def forward(self, x):
h = self.dropout(self.lrelu(self.fc1(x)))
h = self.dropout(self.lrelu(self.fc2(h)))
s = self.sigmoid(self.disc_linear(h))
a = self.aux_linear(h)
return s,a
class MLP_3HL_CRITIC(nn.Module):
def __init__(self, opt):
super(MLP_3HL_CRITIC, self).__init__()
self.fc1 = nn.Linear(opt.resSize + opt.attSize, opt.ndh)
self.fc2 = nn.Linear(opt.ndh, opt.ndh)
self.fc3 = nn.Linear(opt.ndh, opt.ndh)
self.fc4 = nn.Linear(opt.ndh, 1)
self.lrelu = nn.LeakyReLU(0.2, True)
self.apply(weights_init)
def forward(self, x, att):
h = torch.cat((x, att), 1)
h = self.lrelu(self.fc1(h))
h = self.lrelu(self.fc2(h))
h = self.lrelu(self.fc3(h))
h = self.fc4(h)
return h
class MLP_2HL_CRITIC(nn.Module):
def __init__(self, opt):
super(MLP_2HL_CRITIC, self).__init__()
self.fc1 = nn.Linear(opt.resSize + opt.attSize, opt.ndh)
self.fc2 = nn.Linear(opt.ndh, opt.ndh)
self.fc3 = nn.Linear(opt.ndh, 1)
self.lrelu = nn.LeakyReLU(0.2, True)
self.apply(weights_init)
def forward(self, x, att):
h = torch.cat((x, att), 1)
h = self.lrelu(self.fc1(h))
h = self.lrelu(self.fc2(h))
h = self.fc3(h)
return h
class MLP_2HL_Dropout_CRITIC(nn.Module):
def __init__(self, opt):
super(MLP_2HL_Dropout_CRITIC, self).__init__()
self.fc1 = nn.Linear(opt.resSize + opt.attSize, opt.ndh)
self.fc2 = nn.Linear(opt.ndh, opt.ndh)
self.fc3 = nn.Linear(opt.ndh, 1)
self.lrelu = nn.LeakyReLU(0.2, True)
self.dropout = nn.Dropout(p=0.5)
self.apply(weights_init)
def forward(self, x, att):
h = torch.cat((x, att), 1)
h = self.dropout(self.lrelu(self.fc1(h)))
h = self.dropout(self.lrelu(self.fc2(h)))
h = self.fc3(h)
return h
class MLP_CRITIC(nn.Module):
def __init__(self, opt):
super(MLP_CRITIC, self).__init__()
self.fc1 = nn.Linear(opt.resSize + opt.attSize, opt.ndh)
#self.fc2 = nn.Linear(opt.ndh, opt.ndh)
self.fc2 = nn.Linear(opt.ndh, 1)
self.lrelu = nn.LeakyReLU(0.2, True)
self.apply(weights_init)
def forward(self, x, att):
h = torch.cat((x, att), 1)
h = self.lrelu(self.fc1(h))
h = self.fc2(h)
return h
class MLP_D(nn.Module):
def __init__(self, opt):
super(MLP_D, self).__init__()
self.fc1 = nn.Linear(opt.resSize + opt.attSize, opt.ndh)
self.fc2 = nn.Linear(opt.ndh, 1)
self.lrelu = nn.LeakyReLU(0.2, True)
self.sigmoid = nn.Sigmoid()
self.apply(weights_init)
def forward(self, x, att):
h = torch.cat((x, att), 1)
h = self.lrelu(self.fc1(h))
h = self.sigmoid(self.fc2(h))
return h
class MLP_2HL_Dropout_G(nn.Module):
def __init__(self, opt):
super(MLP_2HL_Dropout_G, self).__init__()
self.fc1 = nn.Linear(opt.attSize + opt.nz, opt.ngh)
self.fc2 = nn.Linear(opt.ngh, opt.ngh)
self.fc3 = nn.Linear(opt.ngh, opt.resSize)
self.lrelu = nn.LeakyReLU(0.2, True)
#self.prelu = nn.PReLU()
self.relu = nn.ReLU(True)
self.dropout = nn.Dropout(p=0.5)
self.apply(weights_init)
def forward(self, noise, att):
h = torch.cat((noise, att), 1)
h = self.dropout(self.lrelu(self.fc1(h)))
h = self.dropout(self.lrelu(self.fc2(h)))
h = self.relu(self.fc3(h))
return h
class MLP_3HL_G(nn.Module):
def __init__(self, opt):
super(MLP_3HL_G, self).__init__()
self.fc1 = nn.Linear(opt.attSize + opt.nz, opt.ngh)
self.fc2 = nn.Linear(opt.ngh, opt.ngh)
self.fc3 = nn.Linear(opt.ngh, opt.ngh)
self.fc4 = nn.Linear(opt.ngh, opt.resSize)
self.lrelu = nn.LeakyReLU(0.2, True)
#self.prelu = nn.PReLU()
self.relu = nn.ReLU(True)
self.apply(weights_init)
def forward(self, noise, att):
h = torch.cat((noise, att), 1)
h = self.lrelu(self.fc1(h))
h = self.lrelu(self.fc2(h))
h = self.lrelu(self.fc3(h))
h = self.relu(self.fc4(h))
return h
class MLP_2HL_G(nn.Module):
def __init__(self, opt):
super(MLP_2HL_G, self).__init__()
self.fc1 = nn.Linear(opt.attSize + opt.nz, opt.ngh)
self.fc2 = nn.Linear(opt.ngh, opt.ngh)
self.fc3 = nn.Linear(opt.ngh, opt.resSize)
self.lrelu = nn.LeakyReLU(0.2, True)
#self.prelu = nn.PReLU()
self.relu = nn.ReLU(True)
self.apply(weights_init)
def forward(self, noise, att):
h = torch.cat((noise, att), 1)
h = self.lrelu(self.fc1(h))
h = self.lrelu(self.fc2(h))
h = self.relu(self.fc3(h))
return h
class MLP_Dropout_G(nn.Module):
def __init__(self, opt):
super(MLP_Dropout_G, self).__init__()
self.fc1 = nn.Linear(opt.attSize + opt.nz, opt.ngh)
self.fc2 = nn.Linear(opt.ngh, opt.resSize)
self.lrelu = nn.LeakyReLU(0.2, True)
self.relu = nn.ReLU(True)
self.dropout = nn.Dropout(p=0.2)
self.apply(weights_init)
def forward(self, noise, att):
h = torch.cat((noise, att), 1)
h = self.dropout(self.lrelu(self.fc1(h)))
h = self.relu(self.fc2(h))
return h
class MLP_G(nn.Module):
def __init__(self, opt):
super(MLP_G, self).__init__()
self.fc1 = nn.Linear(opt.attSize + opt.nz, opt.ngh)
self.fc2 = nn.Linear(opt.ngh, opt.resSize)
self.lrelu = nn.LeakyReLU(0.2, True)
#self.prelu = nn.PReLU()
self.relu = nn.ReLU(True)
self.apply(weights_init)
def forward(self, noise, att):
h = torch.cat((noise, att), 1)
h = self.lrelu(self.fc1(h))
h = self.relu(self.fc2(h))
return h
class MLP_2048_1024_Dropout_G(nn.Module):
def __init__(self, opt):
super(MLP_2048_1024_Dropout_G, self).__init__()
self.fc1 = nn.Linear(opt.attSize + opt.nz, opt.ngh)
#self.fc2 = nn.Linear(opt.ngh, opt.ngh)
self.fc2 = nn.Linear(opt.ngh, 1024)
self.fc3 = nn.Linear(1024, opt.resSize)
self.lrelu = nn.LeakyReLU(0.2, True)
#self.prelu = nn.PReLU()
#self.relu = nn.ReLU(True)
self.dropout = nn.Dropout(p=0.5)
self.apply(weights_init)
def forward(self, noise, att):
h = torch.cat((noise, att), 1)
h = self.dropout(self.lrelu(self.fc1(h)))
h = self.dropout(self.lrelu(self.fc2(h)))
h = self.fc3(h)
return h
class MLP_SKIP_G(nn.Module):
def __init__(self, opt):
super(MLP_SKIP_G, self).__init__()
self.fc1 = nn.Linear(opt.attSize + opt.nz, opt.ngh)
#self.fc2 = nn.Linear(opt.ngh, opt.ngh)
#self.fc2 = nn.Linear(opt.ngh, 1024)
self.fc2 = nn.Linear(opt.ngh, opt.resSize)
self.fc_skip = nn.Linear(opt.attSize, opt.resSize)
self.lrelu = nn.LeakyReLU(0.2, True)
#self.prelu = nn.PReLU()
self.relu = nn.ReLU(True)
self.apply(weights_init)
def forward(self, noise, att):
h = torch.cat((noise, att), 1)
h = self.lrelu(self.fc1(h))
#h = self.lrelu(self.fc2(h))
h = self.relu(self.fc2(h))
h2 = self.fc_skip(att)
return h+h2
class MLP_SKIP_D(nn.Module):
def __init__(self, opt):
super(MLP_SKIP_D, self).__init__()
self.fc1 = nn.Linear(opt.resSize + opt.attSize, opt.ndh)
self.fc2 = nn.Linear(opt.ndh, 1)
self.fc_skip = nn.Linear(opt.attSize, opt.ndh)
self.lrelu = nn.LeakyReLU(0.2, True)
self.sigmoid = nn.Sigmoid()
self.apply(weights_init)
def forward(self, x, att):
h = torch.cat((x, att), 1)
h = self.lrelu(self.fc1(h))
h2 = self.lrelu(self.fc_skip(att))
h = self.sigmoid(self.fc2(h+h2))
return h
| [
"noreply@github.com"
] | noreply@github.com |
0020b9b9b399b772a1554cd04d96f72050d68d34 | 41cc033f82ce2b134cfeb71bbea2e5a369b1ba5d | /vise/analyzer/dos_plotter.py | 415b848af07e3986d22b4b100f92b1c192a80baf | [] | no_license | takahashi-akira-36m/vise_test | 0fad2087b5503d40592af7b769069b641ab3b821 | e96f9ac914b023b330a26a43610a4b331af36ade | refs/heads/master | 2023-01-07T16:16:53.770431 | 2019-12-17T02:49:51 | 2019-12-17T02:49:51 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 15,512 | py | # -*- coding: utf-8 -*-
from collections import OrderedDict, defaultdict
import numpy as np
from atomate.utils.utils import get_logger
from pymatgen.electronic_structure.core import Spin
from pymatgen.electronic_structure.dos import Dos
from pymatgen.electronic_structure.dos import add_densities
from pymatgen.electronic_structure.plotter import DosPlotter
from pymatgen.io.vasp import Vasprun
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
from vise.config import SYMMETRY_TOLERANCE, ANGLE_TOL
__author__ = "Yu Kumagai"
__maintainer__ = "Yu Kumagai"
logger = get_logger(__name__)
class ViseDosPlotter(DosPlotter):
def get_plot(self, xlim=None, ylims=None, cbm_vbm=None, legend=True,
crop_first_value=False, title=None):
"""
Get a matplotlib plot showing the DOS.
Args:
xlim (list):
Specifies the x-axis limits. Set to None for automatic
determination.
ylims (list):
Specifies the y-axes limits. Two types of input.
[[y1min, y1max], [y2min, y2max], ..]
cbm_vbm (list):
Specify cbm and vbm [cbm, vbm]
legend (bool):
Whether to show the figure legend.
crop_first_value (bool):
Whether to crop the fist DOS.
title (str):
Title of the figure
"""
ncolors = max(3, len(self._doses))
ncolors = min(9, ncolors)
import palettable
colors = palettable.colorbrewer.qualitative.Set1_9.mpl_colors
y = None
all_densities = []
all_energies = []
# The DOS calculated using VASP holds a spuriously large value at the
# first mesh to keep the consistency with the integrated DOS in DOSCAR
# file. An example is shown below.
# ------------- DOSCAR --------------------
# 10 10 1 0
# 0.1173120E+02 0.5496895E-09 0.5496895E-09 0.5496895E-09 0.5000000E-15
# 1.000000000000000E-004
# CAR
# unknown system
# 23.00000000 - 9.00000000 3201 6.62000004 1.00000000
# -9.000 0.6000E+03 0.6000E+03 0.6000E+01 0.6000E+01 <-- large DOS
# -8.990 0.0000E+00 0.0000E+00 0.6000E+01 0.6000E+01
i = 1 if crop_first_value else 0
for key, dos in self._doses.items():
energies = dos['energies'][i:]
densities = {Spin(k): v[i:] for k, v in dos['densities'].items()}
if not y:
y = {Spin.up: np.zeros(energies.shape),
Spin.down: np.zeros(energies.shape)}
all_energies.append(energies)
all_densities.append(densities)
# Make groups to be shown in the same figure.
# Example, ZrTiSe4
# keys = ['Total', 'Site:1 Zr-s', 'Site:1 Zr-p', 'Site:1 Zr-d',
# 'Site:2 Ti-s', 'Site:2 Ti-p', 'Site:2 Ti-d', 'Site:3 Se-s',
# 'Site:3 Se-p', 'Site:3 Se-d', 'Site:5 Se-s', 'Site:5 Se-p',
# 'Site:5 Se-d']
keys = list(self._doses.keys())
grouped_keys = OrderedDict()
for k in keys:
first_word = k.split()[0]
if first_word in grouped_keys:
grouped_keys[first_word].append(k)
else:
grouped_keys[first_word] = [k]
import matplotlib.pyplot as plt
num_figs = len(grouped_keys)
fig, axs = plt.subplots(num_figs, 1, sharex=True)
if xlim:
axs[0].set_xlim(xlim)
n = 0
for i, gk in enumerate(grouped_keys):
all_pts = []
for j, key in enumerate(grouped_keys[gk]):
x = []
y = []
for spin in [Spin.up, Spin.down]:
if spin in all_densities[n]:
densities = list(int(spin) * all_densities[n][spin])
energies = list(all_energies[n])
x.extend(energies)
y.extend(densities)
all_pts.extend(list(zip(x, y)))
axs[i].plot(x, y, color=colors[j % ncolors], label=str(key),
linewidth=2)
n += 1
# plot vertical lines for band edges or Fermi level
if self.zero_at_efermi:
# plot a line
axs[i].axvline(0, color="black", linestyle="--", linewidth=0.5)
if cbm_vbm:
axs[i].axvline(cbm_vbm[0] - cbm_vbm[1], color="black",
linestyle="--", linewidth=0.5)
else:
axs[i].axvline(self._doses[key]['efermi'],
color="black", linestyle="--", linewidth=0.5)
if cbm_vbm:
axs[i].axvline(cbm_vbm[0], color="black", linestyle="--",
linewidth=0.5)
if legend:
axs[i].legend(loc="best", markerscale=0.1)
# axs[i].legend(bbox_to_anchor=(1.1, 0.8), loc="best")
leg = axs[i].get_legend()
for legobj in leg.legendHandles:
legobj.set_linewidth(1.2)
ltext = leg.get_texts()
plt.setp(ltext, fontsize=7)
else:
axs[i].set_title(key, fontsize=7)
axs[i].axhline(0, color="black", linewidth=0.5)
if ylims and len(ylims) not in (num_figs, 2):
raise ValueError("The number of y-ranges is not proper.")
if ylims and len(ylims) == 2:
if ylims[0][1] > 1.0:
axs[0].set_ylim(ylims[0])
for i in range(1, len(axs)):
axs[i].set_ylim(ylims[1])
elif ylims:
for i in range(len(axs)):
axs[i].set_ylim(ylims[i])
# else:
# for i in range(len(axs)):
# ylim = axs[i].get_ylim()
# print(ylim)
# relevanty = [p[1] for p in all_pts
# if ylim[0] < p[0] < ylim[1]]
# axs[i].set_ylim((min(relevanty), max(relevanty)))e
axs[-1].set_xlabel('Energy (eV)')
plt.tight_layout()
plt.subplots_adjust(left=None, bottom=None, right=None, top=None,
wspace=0.2, hspace=0.2)
if title:
axs[0].title.set_text(title)
return plt
def get_dos_plot(vasprun_file: str,
cbm_vbm: list = None,
pdos_type: str = "element",
specific: list = None,
orbital: list = True,
xlim: list = None,
ymaxs: list = None,
zero_at_efermi: bool = True,
legend: bool = True,
crop_first_value: bool = True,
show_spg: bool = True,
symprec: float = SYMMETRY_TOLERANCE,
angle_tolerance: float = ANGLE_TOL):
"""
Args:
vasprun_file (str):
vasprun.xml-type file name
cbm_vbm (list):
List of [cbm, vbm]
pdos_type (str): Plot type of PDOS.
"element": PDOS grouped by element type
"site": PDOS grouped by equivalent sites
"none": PDOS are not grouped.
specific (list): Show specific PDOS. If list elements are integers,
PDOS at particular sites are shown. If elements are shown,
PDOS of particular elements are shown.
["1", "2"] --> At site 1 and 2 compatible with pdos_type = "none"
["Mg", "O"] --> Summed at Mg and O sites coompatible with
pdos_type = "element"
orbital (bool):
Whether to show orbital decomposed PDOS.
xlim (list):
Specifies the x-axis limits. Set to None for automatic determination.
ymaxs (list):
Specifies the maxima of absolute y-axis limits.
zero_at_efermi (bool):
Whether to show the plot in the absolute scale.
legend (bool):
Whether to show the figure legend.
crop_first_value (bool):
Whether to crop the fist DOS.
show_spg (bool):
Whether to show space group number in the title.
symprec (float):
Symprec for determining the equivalent sites.
"""
v = Vasprun(vasprun_file, ionic_step_skip=True, parse_eigen=False)
if v.converged_electronic is False:
logger.warning("SCF is not attained in the vasp calculation.")
complete_dos = v.complete_dos
# check cbm
if cbm_vbm is None:
if complete_dos.get_gap() > 0.1:
cbm_vbm = complete_dos.get_cbm_vbm()
structure = v.final_structure
dos = OrderedDict()
# The CompleteDos behaves as DOS for total dos.
dos["Total"] = complete_dos
if specific and specific[0].isdigit():
if pdos_type is not "none":
logger.warning("pdos_type is changed from {} to none"
.format(pdos_type))
pdos_type = "none"
elif specific and specific[0].isalpha():
if pdos_type is not "none":
logger.warning("pdos_type is changed from {} to element"
.format(pdos_type))
pdos_type = "element"
sga = None
grouped_indices = defaultdict(list)
if pdos_type == "element":
for indices, s in enumerate(structure):
grouped_indices[str(s.specie)].append(indices)
elif pdos_type == "site":
# equivalent_sites: Equivalent site indices from SpacegroupAnalyzer.
sga = SpacegroupAnalyzer(structure=structure,
symprec=symprec,
angle_tolerance=angle_tolerance)
symmetrized_structure = sga.get_symmetrized_structure()
# equiv_indices = [[0], [1], [2, 3], [4, 5]]
equiv_index_lists = symmetrized_structure.equivalent_indices
for l in equiv_index_lists:
name = str(structure[l[0]].specie) + " " \
+ sga.get_symmetry_dataset()["wyckoffs"][l[0]]
grouped_indices[name] = l
elif pdos_type == "none":
for indices, s in enumerate(structure):
grouped_indices[str(s.specie) + " site:" + str(indices)].append(indices)
else:
raise KeyError("The given pdos_type is not supported.")
# TODO: Add specific handling
# if specific:
# tmp = defaultdict(list)
# for key, value in grouped_indices.items():
# if pdos_type == "element" and key in specific:
# tmp[key] = value
# else:
# # type(index) is str
# index = ''.join(c for c in key if c.isdigit())
# if index in specific:
# tmp[key] = value
# grouped_indices = tmp
# efermi is set to VBM if exists.
efermi = cbm_vbm[1] if cbm_vbm else complete_dos.efermi
complete_dos.efermi = efermi
energies = complete_dos.energies
for key, value in grouped_indices.items():
for indices in value:
site = structure[indices]
if orbital:
for orb, pdos in complete_dos.get_site_spd_dos(site).items():
# " " is used for grouping the plots.
if pdos_type == "none":
name = key + " " + str(orb)
else:
name = \
key + " #" + str(len(value)) + " " + str(orb)
density = divide_densities(pdos.densities, len(value))
if name in dos:
density = add_densities(dos[name].densities, density)
dos[name] = Dos(efermi, energies, density)
else:
dos[name] = Dos(efermi, energies, density)
else:
name = key + "(" + str(len(key)) + ")"
pdos = complete_dos.get_site_dos(site)
if name in dos:
dos[name] = add_densities(dos[name], pdos)
else:
dos[name] = pdos
# use complete_dos.efermi for total dos.
plotter = ViseDosPlotter(zero_at_efermi=zero_at_efermi)
plotter.add_dos_dict(dos)
if xlim is None:
xlim = [-10, 10]
if ymaxs:
ylims = [[-y, y] for y in ymaxs] \
if v.incar.get("ISPIN", 1) == 2 else [[0, y] for y in ymaxs]
else:
energies = complete_dos.energies - efermi
tdos_max = max_density(complete_dos.densities, energies, xlim,
crop_first_value)
tdos_max *= 1.1
ylims = [[-tdos_max, tdos_max]] if v.incar.get("ISPIN", 1) == 2 \
else [[0, tdos_max]]
pdos_max = 0.0
for k, d in dos.items():
if k == "Total":
continue
pdos_max = \
max(max_density(d.densities, energies, xlim), pdos_max)
pdos_max *= 1.1
ylims.append([-pdos_max, pdos_max] if v.incar.get("ISPIN", 1) == 2
else [0, pdos_max])
print("y-range", ylims)
if show_spg:
if sga is None:
sga = SpacegroupAnalyzer(structure, symprec=symprec)
sg_num_str = str(sga.get_space_group_number())
sg = f" {sga.get_space_group_symbol()} ({sg_num_str})"
print(f"Space group number: {sg}")
title = f"{structure.composition} SG: {sg}"
else:
title = str(structure.composition)
return plotter.get_plot(xlim=xlim,
ylims=ylims,
cbm_vbm=cbm_vbm,
legend=legend,
crop_first_value=crop_first_value,
title=title)
def divide_densities(density: dict,
denominator: float):
"""
Method to sum two densities.
Args:
density: First density.
denominator: Second density.
Returns:
{spin: np.array(density)}.
"""
return {spin: np.array(value) / denominator
for spin, value in density.items()}
def max_density(density: dict,
energies: list,
xlim: list,
crop_first_value: bool = True) -> float:
"""
Method to sum two densities.
Args:
density (dict):
Note that the first value may contains huge values when the
lower limit of the calculation of density of states is larger than
that of occupied states. Therefore, we need to crop the first value
by default.
{Spin.up: [...], Spin.down: [...] }
energies (list):
Energy mesh
xlim (list):
Limit of x-range.
[x-min, x-max]
crop_first_value (bool):
Whether to crop the first value or not.
Return:
Max value in the density within the given x-range.
"""
values = []
for density_in_each_spin in density.values():
for i, (d, e) in enumerate(zip(density_in_each_spin, energies)):
if crop_first_value and i == 0:
continue
if xlim[0] < e < xlim[1]:
values.append(d)
if not values:
raise ValueError("DOS is empty at the given energy {0[0]} - {0[1]} "
"range.".format(xlim))
return max(values)
| [
"yuuukuma@gmail.com"
] | yuuukuma@gmail.com |
b4dbf4f6ea8e581805b29d6a2b0247bb430e5d74 | 38afaa5e36ccfea634bf9e5afc1fb6dc8bd12dfe | /accu_2.py | f56413e883c43be9d1d5a06750a18b5c8ef0411b | [] | no_license | alpintrekker/instrumentation-tools | 81eef5c26b7d3f3c762bc8d65431e6b1752d2ee5 | de5e2882c6d13858954de159602f1d2868f63106 | refs/heads/master | 2021-01-11T02:17:21.342346 | 2017-08-26T06:18:41 | 2017-08-26T06:18:41 | 70,984,037 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,298 | py | from Adafruit_BBIO.SPI import SPI
import struct
import time
spi = SPI(0,0)
spi.mode = 1
spi.msh = 300000
spi.xfer([0b00011000, 0x02])
def decode_sent_command(ser_cmd):
cmd_bits = (ser_cmd & (((1<<6) - 1)<<10))>>10
data_bits = ser_cmd & ((1<<10)-1)
return bin(cmd_bits)[2:], data_bits
def send_cmd(cmd,data):
ser_cmd = (cmd << 10) | (data & ((1<<10)-1))
w0 = ser_cmd >> 8
w1 = ser_cmd & ((1<<8)-1)
return [w0, w1]
# write to RDAC
spi.xfer(list(struct.unpack('BB', struct.pack('>H', 1 << 10 | 0))))
# read RDAC
spi.xfer(list(struct.unpack('BB', struct.pack('>H', 0b10 << 10 | 0))))
# read Control register
spi.xfer(list(struct.unpack('BB', struct.pack('>H', 0b111 << 10 | 0))))
spi.readbytes(2)
for i in range(1024):
spi.xfer(list(struct.unpack('BB', struct.pack('>H', 1 << 10 | i))))
time.sleep(.2)
spi.writebytes([0x0,0x0])
spi.writebytes([0x18,0x02])
spi.writebytes([0x05,0x00])
spi.writebytes([0x08,0x0])
spi.writebytes([0x0,0x0])
spi.writebytes([0x0,0x0])
spi.writebytes([0x0,0x0])
spi.xfer([0x0,0x0])
spi.xfer([0x18,0x02])
spi.xfer([0x05,0x00])
spi.xfer([0x08,0x0])
spi.xfer([0x0,0x0])
class voltage_divider:
def __init__(self):
self.R1 = 1e20
self.R2 = 1e20
| [
"alpintrekker@gmail.com"
] | alpintrekker@gmail.com |
aa67f88b1322ed5544f212745020fa13656c378b | 6f645a5aadda643948b543fbb86e82710918d5e4 | /imgmeta/migrations/0001_initial.py | 2c9952c1cfe391e0a510c43716bd279f86c485c8 | [] | no_license | Abdul-Marzouq/Cloud-Computing-Assignment-2 | 5d0fae4684e9a5e702d0f2dbff430924d329f9d6 | 3cf08dbd22ac96a107f091f39f5ff47391acf0d8 | refs/heads/master | 2023-01-14T12:16:17.737380 | 2020-11-19T10:12:45 | 2020-11-19T10:12:45 | 311,052,363 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 506 | py | # Generated by Django 3.1.3 on 2020-11-07 15:51
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='ImageSet',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('image', models.ImageField(null=True, upload_to='imgset/')),
],
),
]
| [
"abdulmarzouq@gmail.com"
] | abdulmarzouq@gmail.com |
dc440447ca5ed59e3783bde48e4899e3be8df4c9 | ef18630909cb771f169e1f0f3aa6fbb7818c7bcd | /play_digits.py | b800343bb337d9e81a8d49f5cb7741fddb8513d1 | [] | no_license | nickbonne/code_wars_solutions | b656ac63803c2777c2c998223997d173da701827 | 8caa9078241773eb91c4b7cb05def259695f7cb6 | refs/heads/master | 2021-01-20T09:13:25.258497 | 2017-09-01T00:17:37 | 2017-09-01T00:17:37 | 101,582,134 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 353 | py |
# https://www.codewars.com/kata/playing-with-digits/train/python
def dig_pow(n, p):
result = zip(
[int(x) for x in str(n)],
[x for x in range(p, p + (len(str(n))))]
)
result = sum([x[0] **x[1] for x in list(result)])
if result % n == 0:
return int(result / n)
else:
return -1
| [
"nickbonne@gmail.com"
] | nickbonne@gmail.com |
0021c7e9e93f3bb30c1d2f4511b9a15aee101958 | a00c487d88c50401ebf8505cd267c70b42e3c362 | /bangla/soup/MSR.py | 9c488a0e43064bf12f84eebfbeb665415f8376dd | [] | no_license | sharif1302042/A-news-Agrregation-system | 9aca07ed29f13b5da8e93a2aabe03281d6b66365 | 5e48a726f5fedba686d18601d561784c6ceddd5a | refs/heads/master | 2020-04-01T11:00:39.088387 | 2019-11-09T15:05:24 | 2019-11-09T15:05:24 | 153,142,416 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 936 | py | import requests
from bs4 import BeautifulSoup
news=[]
r=requests.get('https://bangla.bdnews24.com/politics/')
soup = BeautifulSoup(r.text, 'html.parser')
r1=soup.find_all('li',attrs={'class':'article first '})
r2=soup.find_all('li',attrs={'class':'article '})
l=0
for r in r1+r2:
if l<10:
txt=r.find('a')['href']
url=r.find('a').text[1:-1]
news.append((url,txt,'Bdnews24'))
l+=1
"""
#--------------jugantor-----------
r=requests.get('https://www.jugantor.com/')
soup = BeautifulSoup(r.text, 'html.parser')
r1=soup.find_all('div',attrs={'id':'popular_list_block'})
url=r1[0].find('a')
r=r1[0].find('a')
txt=r.find('h4').text
news.append((txt,url,"Jugantor"))
r1=soup.find_all('div',attrs={'class':'editor_picks_list'})
l=0
for r in r1:
if l<6:
url=r.find('a')['href']
txt=r.find('a')
txt=txt.find('h4').text
news.append((txt,url,"Jugantor"))
l+=1
print('MSR',len(news))
for r in news:
print(r[0])
""" | [
"sharifulcsehstu@gmail.com"
] | sharifulcsehstu@gmail.com |
087e92e25d5452f986b22430ce4fffefb538f075 | 0b49c40162e15b5e0c551e548d865c4105e8df7d | /koopmanInvertedPendulum.py | 23a2317318f8c0da4234581463207c14b2bd54f1 | [] | no_license | jiemingChen/DeepKoopman | 654a47922e4d7d6161c032a5e7ac7374d6999917 | 2e6ce8218c0bf5b7bcb072a6983a8f6870ec6186 | refs/heads/master | 2023-03-27T22:39:24.992333 | 2020-09-28T23:11:40 | 2020-09-28T23:11:40 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,539 | py | import numpy as np
import torch
import torch.nn as nn
import gym
from torch.utils.data import Dataset, DataLoader
import control
import os
from ReplayBuffer import ReplayBuffer
import time
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--env_name", default='InvertedPendulum-v2')
parser.add_argument("--max_iter", default=200)
parser.add_argument("--hidden_dim", default=3, type=int)
parser.add_argument("--mode", default="train")
args = parser.parse_args()
class DeepKoopman():
def __init__(self, env_name = "Pendulum-v0", hidden_dim = 2):
self.env_name = env_name
self.env = gym.make(env_name)
self.state_dim = self.env.observation_space.shape[0]+1
self.hidden_dim = hidden_dim
self.action_dim = self.env.action_space.shape[0]
self.encoder = nn.Sequential(nn.Linear(self.state_dim, 16),
nn.PReLU(),
nn.Linear(16, 16),
nn.PReLU(),
nn.Linear(16, hidden_dim))
self.decoder = nn.Sequential(nn.Linear(hidden_dim, 16),
nn.PReLU(),
nn.Linear(16, 16),
nn.PReLU(),
nn.Linear(16, self.state_dim))
self.propagate = nn.Linear(hidden_dim+self.action_dim, hidden_dim, bias = False)
self.lambda1 = 1.0
self.lambda2 = 0.3
self.replay_buffer = ReplayBuffer(100000)
def get_system(self):
weight = self.propagate.weight.data.numpy()
A = weight[:, :self.hidden_dim]
B = weight[:, self.hidden_dim:]
return A, B
def forward(self, xt, ut):
gt = self.encoder(xt)
xt_ = self.decoder(gt)
gtdot = self.propagate(torch.cat((gt, ut), axis = -1))
gt1 = gt + self.env.env.dt*gtdot
xt1_ = self.decoder(gt1)
return gt, gt1, xt_, xt1_
def save(self):
if not os.path.exists("weights/"):
os.mkdir("weights/")
file_name = "weights/" + self.env_name + ".pt"
torch.save({"encoder" : self.encoder.state_dict(),
"decoder" : self.decoder.state_dict(),
"propagate" : self.propagate.state_dict()}, file_name)
print("save model to " + file_name)
def load(self):
try:
if not os.path.exists("weights/"):
os.mkdir("weights/")
file_name = "weights/" + self.env_name + ".pt"
checkpoint = torch.load(file_name)
self.encoder.load_state_dict(checkpoint["encoder"])
self.decoder.load_state_dict(checkpoint["decoder"])
self.propagate.load_state_dict(checkpoint["propagate"])
print("load model from " + file_name)
except:
print("fail to load model!")
def transform_state(self, x):
return np.array([x[1], np.sin(x[1]), np.cos(x[1]), x[2], x[3]])
def policy_rollout(self):
A, B = self.get_system()
Q = np.eye(self.hidden_dim)
R = np.array([[0.01]])
K, _, _ = control.lqr(A, B, Q, R)
ref = torch.FloatTensor([[0.0, 0.0, 1.0, 0., 0.]])
ref = model.encoder(ref).detach().numpy()
obs_old = self.transform_state(self.env.reset())
#obs_old[2] = obs_old[2] / 8.0
for _ in range(200):
if np.random.random() > 0.05:
state = torch.FloatTensor(obs_old.reshape((1, -1)))
y = model.encoder(state).detach().numpy()
action = -np.dot(K, (y-ref).T)
action = np.clip(np.array([action.item()]), -1., 1.)
else:
action = self.env.action_space.sample()
#self.env.render()
obs, reward, done, info = self.env.step(action)
#obs[2] = obs[2] / 8.0
obs = self.transform_state(obs)
self.replay_buffer.push((obs_old, action, obs))
obs_old = obs
def random_rollout(self):
obs_old = self.transform_state(self.env.reset())
#obs_old[2] = obs_old[2] / 8.
for _ in range(200):
action = self.env.action_space.sample()
obs, reward, done, info = self.env.step(action)
obs = self.transform_state(obs)
#obs[2] = obs[2] / 8.0
self.replay_buffer.push((obs_old, action, obs))
obs_old = obs
def train(self, max_iter, lr =0.001):
mseloss = nn.MSELoss()
l1loss = nn.L1Loss()
encoder_optimizer = torch.optim.Adam(self.encoder.parameters(), lr = lr)
decoder_optimizer = torch.optim.Adam(self.decoder.parameters(), lr = lr)
propagate_optimizer = torch.optim.Adam(self.propagate.parameters(), lr = lr)
for i in range(20):
self.random_rollout()
for it in range(max_iter):
loss_hist = []
for _ in range(100):
xt, ut, xt1 = self.replay_buffer.sample(64)
xt = torch.FloatTensor(xt)
ut = torch.FloatTensor(ut)
xt1 = torch.FloatTensor(xt1)
gt, gt1, xt_, xt1_ = self.forward(xt, ut)
ae_loss = mseloss(xt_, xt)
pred_loss = mseloss(xt1_, xt1)
metric_loss = l1loss(torch.norm(gt1-gt, dim=1), torch.norm(xt1-xt, dim=1))
#reg_loss = torch.norm(self.propagate.weight.data[:, self.hidden_dim:])
total_loss = ae_loss + self.lambda1*pred_loss + self.lambda2*metric_loss
encoder_optimizer.zero_grad()
decoder_optimizer.zero_grad()
propagate_optimizer.zero_grad()
total_loss.backward()
encoder_optimizer.step()
decoder_optimizer.step()
propagate_optimizer.step()
loss_hist.append(total_loss.detach().numpy())
print("epoch: %d, loss: %2.5f" % (it, np.mean(loss_hist)))
for i in range(5):
self.policy_rollout()
for i in range(5):
self.random_rollout()
if __name__ == "__main__":
model = DeepKoopman(args.env_name, args.hidden_dim)
if args.mode == "train":
model.train(args.max_iter, 0.001)
model.save()
else:
model.load()
A, B = model.get_system()
Q = np.eye(args.hidden_dim)
R = np.array([[0.08]])
K, _, _ = control.lqr(A, B, Q, R)
print(A)
print(B)
print(K)
env = gym.make(args.env_name)
ref = torch.FloatTensor([[0.0, 0.0, 1.0, 0., 0.]])
ref = model.encoder(ref).detach().numpy()
offset = [0.1, 0.2, 0.3, 0.4, 0.5]
for k in range(5):
state = env.reset()
state[1] = offset[k]
env.env.set_state(state[:2], state[:2])
state = model.transform_state(state)
for i in range(200):
env.render()
state = torch.FloatTensor(state.reshape((1, -1)))
#state[0, 2] = state[0, 2] / 8.0
y = model.encoder(state).detach().numpy()
action = -np.dot(K, (y-ref).T)
state, reward, done, info = env.step(action)
#print(state)
state = model.transform_state(state)
env.close()
| [
"csj15thu@gmail.com"
] | csj15thu@gmail.com |
fa9510bc5a544b208da9c4746353e04ee9eb0bab | aa9718aa4c13bc681e4a92b68f5dfa53ae528812 | /bert_multilabel/src/extract_reviews_text.py | 77b62ed3ca15b2439698f17519497d8faf647710 | [] | no_license | pj0616/med_project | 58a356929b978108c6027637b8e0e5edd452c3b4 | c91d38d46670c28c264ddc3c7acc43d9f5c30ef6 | refs/heads/master | 2022-12-31T08:03:01.369370 | 2020-10-20T09:08:53 | 2020-10-20T09:08:53 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 508 | py | import os
import pandas as pd
def main():
input_csv_path = r"../otzovik_csvs/fold_4/dev.csv"
output_dir = r'../otzovik_csvs_texts/fold_4'
if not os.path.exists(output_dir):
os.makedirs(output_dir)
output_fname = 'dev.csv'
output_path = os.path.join(output_dir, output_fname)
reviews_df = pd.read_csv(input_csv_path, encoding="utf-8")
review_texts_df = reviews_df['sentences']
review_texts_df.to_csv(output_path, index=False)
if __name__ == '__main__':
main()
| [
"an@clab"
] | an@clab |
94edf1ad6adc7d8a3551c8b9103bf294c8afc731 | cd23b0457bc02a60b89f1f52783e56cc36d85b5e | /oop/getitem.py | 1bff5722f8bb3e1db032216968dc42463cf0a724 | [] | no_license | cluo/learingPython | 65c7068613e1a2ae0178e23770503043d9278c45 | 54609288e489047d4dd1dead5ac142f490905f0e | refs/heads/master | 2020-04-01T13:04:15.981758 | 2015-02-23T13:21:31 | 2015-02-23T13:21:31 | 28,440,969 | 4 | 0 | null | null | null | null | UTF-8 | Python | false | false | 401 | py | #!/usr/bin/evn python
#-*- coding:utf-8 -*-
__author__ = 'admin'
class Indexer:
def __getitem__(self, index):
return index ** 2
X = Indexer()
print X[2]
for i in range(5):
print(X[i])
class stepper:
def __getitem__(self, i):
return self.data[i]
X = stepper()
X.data = 'spam'
for item in X:
print(item)
print 'p' in X
print [c for c in X]
print ''.join(X)
print list(X)
print tuple(X)
| [
"luosheng@meizu.com"
] | luosheng@meizu.com |
02e1e7d5169dd69414260d0b35f9c75724cc9356 | 5138e7812d7f12bdaf4c9ce345053adcc6b9bbfa | /tests/enc_dec.py | 5eceea53b2be77e3c9abddb70295063cee905dda | [] | no_license | Vovchik22/consensus-mvp | a01e049cedae5173e16dc94d6c5cf8ba6977c58d | 838413ad4ac70779ac936ddae9aeaa164cac8751 | refs/heads/master | 2020-03-18T20:08:37.534815 | 2018-05-28T18:24:22 | 2018-05-28T18:24:22 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 417 | py | import unittest
from Crypto.Hash import SHA256
from crypto.enc_random import enc_part_random
from crypto.dec_part_random import dec_part_random
class EncDec(unittest.TestCase):
def test_enc_dec(self):
era_hash = SHA256.new(b"323423").digest()
enc_data, key = enc_part_random(era_hash)
res_era_hash, rand = dec_part_random(enc_data, key)
self.assertEqual(era_hash, res_era_hash)
| [
"sobol@pandora.foundation"
] | sobol@pandora.foundation |
9114d28401f59341504be6dbb5130626b1a0ad14 | 58411693590a261ea381bb02c39cfb3367fb4efd | /data-science/code.py | 551f4d6785e3a6bfd6db39feb151d482c2592226 | [
"MIT"
] | permissive | rickchaki/greyatom-python-for-data-science | d64440d0e26f47213c086e0e6d03ff72804f5393 | b48b9aa464ace23f113eed9212ec1e9e54682039 | refs/heads/master | 2022-10-04T00:22:45.985613 | 2020-06-03T20:09:51 | 2020-06-03T20:09:51 | 265,033,947 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 816 | py | # --------------
class_1= ['Geoffrey Hinton','Andrew Ng','Sebastian Raschka','Yoshua Bengio']
class_2= ['Hilary Mason','Carla Gentry','Corinna Cortes']
new_class= class_1+class_2
print(new_class)
new_class.pop(-2)
print(new_class)
courses= {'Math':65, 'English':70, 'History':80, 'French': 70, 'Science':60 }
print(courses)
total= 65+70+80+0+70+60
percentage= total/5
print(total, percentage)
mathematics= {'Geoffrey Hinton':78, 'Andrew Ng':95,
'Sebastian Raschka':65,
'Yoshua Benjio':50,
'Hilary Mason':70,
'Corinna Cortes':66,
'Peter Warden':75}
topper = max(mathematics,key = mathematics.get)
print (topper)
first_name=topper.split()[0]
last_name=topper.split()[1]
full_name=last_name+' '+first_name
print(full_name)
certificate_name= full_name.upper()
print(certificate_name)
| [
"rickchaki@users.noreply.github.com"
] | rickchaki@users.noreply.github.com |
d75b6eaedc8a46d16734caaf66860a360d6f0ae2 | 2ad31d0b8e97b95a6fe144a31362af33a1b1c241 | /matplot_learn/python1.py | 9ba4ebd1969807577f501128abd130be37ae2ab9 | [] | no_license | inh3rit/python-learn | 55150eac2b3cd7a7531e5b0f155813eb6544cdd8 | 588ccb9d4ff9b02bbd4a73fad8453b6baf73e287 | refs/heads/master | 2020-03-22T05:07:05.451142 | 2018-07-06T09:32:44 | 2018-07-06T09:32:44 | 139,543,363 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,042 | py | import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-3, 3, 50)
y1 = 2 * x + 1
y2 = x ** 2
plt.figure()
plt.plot(x, y1)
plt.figure(num=3, figsize=(8, 5))
plt.plot(x, y2)
plt.plot(x, y1, color='red', linewidth=1, linestyle='--')
# 设置坐标轴信息
plt.xlim((-1, 2))
plt.ylim((-2, 3))
plt.xlabel('I am x')
plt.ylabel('I am y')
new_ticks = np.linspace(-1, 2, 5)
print(new_ticks)
plt.xticks(new_ticks)
plt.yticks([-2, -1.8, -1, 1.22, 3]
, [r'$really\ bad$', r'$bad\ \alpha$', 'normal', 'good', 'really good'])
# gca = 'get current axis'
ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.spines['bottom'].set_position(('data', 0)) # outward, axes
ax.spines['left'].set_position(('data', 0))
l1, = plt.plot(x, y2, label='up')
l2, = plt.plot(x, y1, color='red', linewidth=1, linestyle='--', label='down')
plt.legend(handles=[l1, l2, ], labels=['aaa', 'bbb', ], loc='best') # 图例
plt.showt()
| [
"391552129@qq.com"
] | 391552129@qq.com |
a1fe20bffdbcd11119758a66cf427cc742d31d68 | fd37bab7db9214267e16e39a177704c421528c20 | /bin/django-admin.py | 55da2438cb2340b1344396138b1d50d981398b23 | [] | no_license | alener/DRFsupervideoblog | 463290b004a780dadc788a43edf7d32f65160218 | 9d8db06631482809a679a5983eed475dbea25329 | refs/heads/master | 2020-03-12T07:26:36.615507 | 2018-04-21T19:35:41 | 2018-04-21T19:35:41 | 130,506,520 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 157 | py | #!/home/alejandro/develop/drfvideoblog/bin/python3
from django.core import management
if __name__ == "__main__":
management.execute_from_command_line()
| [
"alener014@gmail.com"
] | alener014@gmail.com |
5cd8abaa9b036a76810c148b3a3f6234245542b2 | 520c10912b21f5896e816bad3ce2564819540f3a | /myenv/bin/pip | 8bc5cd0b2fa290243926b856e42aa5f4a452cc65 | [] | no_license | ppatel089/Data-Science-Programming | 6dad499f5bb0b7be3b23a3b999e4841aa6c1ea8c | 063e0ed3c40f7d0b2a144577f83009b4445f1bb9 | refs/heads/master | 2020-08-02T18:06:45.648737 | 2020-05-18T03:04:49 | 2020-05-18T03:04:49 | 211,457,987 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 262 | #!/Users/pushparajpatel/Desktop/my_project/myenv/bin/python
# -*- coding: utf-8 -*-
import re
import sys
from pip._internal.cli.main import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())
| [
"pushparajpatel@USF-Gold-Wifi-226-42-173.laptops.usf.edu"
] | pushparajpatel@USF-Gold-Wifi-226-42-173.laptops.usf.edu | |
0b7cad063afdb90a3656d3eb37125ab5c852f506 | 32f5bc330388a96877d93fdd7b21599a40735400 | /Python/SquarePen.py | 844df86ed40e67e7ec69d299a7a585ccec90860e | [] | no_license | alexlwn123/kattis | 670180d86f0863328a16e12ed937c2fefb3226a2 | c1163bae3fdaf95c1087b216c48e7e19059d3d38 | refs/heads/master | 2021-06-21T16:26:15.642449 | 2020-12-24T20:59:10 | 2020-12-24T20:59:10 | 152,286,208 | 1 | 1 | null | 2018-10-14T22:40:09 | 2018-10-09T16:40:48 | Java | UTF-8 | Python | false | false | 121 | py | import sys
def main():
x = int(sys.stdin.readline())
x = (x ** .5)
print(x * 4)
if __name__ == '__main__':
main() | [
"noreply@github.com"
] | noreply@github.com |
6644164ede3bfce872418798918055de3aa91b59 | 67cd873fccf91876d6e65143a9a54acc6e42aed5 | /daily_produce.py | 512b335ab257ffd08c5446313f50d52bc177fa79 | [] | no_license | liuronghu/daily_paper | 708a2cadb0184e094200dc8132fe5afc3178e23f | 743b87e2a5b699f5aa47af5d52345216c4dd42df | refs/heads/master | 2020-07-11T00:54:13.540488 | 2019-08-26T06:47:42 | 2019-08-26T06:47:42 | 204,413,408 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 639 | py | # -*- coding: utf-8 -*-
import pandas as pd
start_date = '28/6/2019'
end_date = '31/7/2019'
target_file = 'daily_file'
source_file = 'daily_template.conf'
source_data = open(source_file, 'rt',encoding='UTF-8').read()
target_data = open(target_file, 'wt',encoding='UTF-8')
date_value = pd.date_range(start_date, end_date)
for date in date_value:
source_data_copy = source_data
source_data_copy = source_data_copy.replace("{year}",str(date.year))
source_data_copy = source_data_copy.replace('{month}',str(date.month))
source_data_copy = source_data_copy.replace('{day}',str(date.day))
target_data.write(source_data_copy) | [
"1281761019@qq.com"
] | 1281761019@qq.com |
39adc6a32c3415477cdde9e47ee24a48124dc2b7 | a24d48fb972556704ba5d062db68c9d1a9125dfb | /aws/app/app.py | 805304b9cb9ab27ac462e41c48ee527d8e9075c0 | [
"MIT"
] | permissive | and-alejandro-arnes/hashicorp-waypoint-demo | 6872fa77f1342b2cc212d286d7997f3f1c51567b | 866ec4e9c5056c7cb606363c436736364b71e565 | refs/heads/main | 2023-06-16T15:11:28.909200 | 2021-07-13T19:50:40 | 2021-07-13T19:50:40 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 352 | py | #!/usr/bin/env python3
from flask import Flask, render_template
app = Flask(__name__)
@app.route('/')
def index():
return render_template('index.html')
@app.route('/health')
def health_check():
return json.dumps({'success':True}), 200, {'ContentType':'application/json'}
if __name__ == '__main__':
app.run(debug=True, host='0.0.0.0')
| [
"mannypotter@hotmail.com"
] | mannypotter@hotmail.com |
5102a13c1af192205b49132a170a820a1c33ee47 | a799a105ab2aba39a475bf2ce086405def0351c2 | /test/model/tpp/common.py | 4fd78b174d605ac5736f5e09c8c9e575b42dfa7b | [
"Apache-2.0"
] | permissive | mbohlkeschneider/gluon-ts | d663750d13798624eca5c9d6f12a87e321ce7334 | df4256b0e67120db555c109a1bf6cfa2b3bd3cd8 | refs/heads/master | 2021-11-24T06:09:49.905352 | 2021-10-14T09:30:38 | 2021-10-14T09:30:38 | 192,546,557 | 54 | 10 | Apache-2.0 | 2022-08-31T18:36:44 | 2019-06-18T13:33:36 | Python | UTF-8 | Python | false | false | 2,364 | py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.
import numpy as np
import pandas as pd
import pytest
from gluonts.dataset.common import ListDataset
def point_process_dataset():
ia_times = np.array([0.2, 0.7, 0.2, 0.5, 0.3, 0.3, 0.2, 0.1])
marks = np.array([0, 1, 2, 0, 1, 2, 2, 2])
lds = ListDataset(
[
{
"target": np.c_[ia_times, marks].T,
"start": pd.Timestamp("2011-01-01 00:00:00", freq="H"),
"end": pd.Timestamp("2011-01-01 03:00:00", freq="H"),
}
],
freq="H",
one_dim_target=False,
)
return lds
def point_process_dataset_2():
lds = ListDataset(
[
{
"target": np.c_[
np.array([0.2, 0.7, 0.2, 0.5, 0.3, 0.3, 0.2, 0.1]),
np.array([0, 1, 2, 0, 1, 2, 2, 2]),
].T,
"start": pd.Timestamp("2011-01-01 00:00:00", freq="H"),
"end": pd.Timestamp("2011-01-01 03:00:00", freq="H"),
},
{
"target": np.c_[
np.array([0.2, 0.1, 0.2, 0.1, 0.3, 0.3, 0.5, 0.4]),
np.array([0, 1, 2, 0, 1, 2, 1, 1]),
].T,
"start": pd.Timestamp("2011-01-01 00:00:00", freq="H"),
"end": pd.Timestamp("2011-01-01 03:00:00", freq="H"),
},
{
"target": np.c_[
np.array([0.2, 0.7, 0.2, 0.5, 0.1, 0.1, 0.2, 0.1]),
np.array([0, 1, 2, 0, 1, 0, 1, 2]),
].T,
"start": pd.Timestamp("2011-01-01 00:00:00", freq="H"),
"end": pd.Timestamp("2011-01-01 03:00:00", freq="H"),
},
],
freq="H",
one_dim_target=False,
)
return lds
| [
"noreply@github.com"
] | noreply@github.com |
5ef248535b30a5327699b7adcb88931292401005 | 83eb5e9459776bb82a9a785be70c5346677ae239 | /PyMeetings.py | 678b5ed63551df06737f1a83209aaa99868d96c9 | [] | no_license | Einlanzerous/PyMeetings | d964f1e1f0393114c3e0484fb025e0820926330b | be690d3da27c8f4bcc562c16ef22ff8f5426f0be | refs/heads/master | 2021-01-19T07:40:26.579190 | 2017-08-17T20:28:50 | 2017-08-17T20:28:50 | 100,642,063 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,013 | py | from PyQt5.QtWidgets import QWidget, QCheckBox, QApplication, QLabel, QLineEdit, QPushButton, QMainWindow
from PyQt5.QtGui import QIcon, QPixmap, QPalette
from PyQt5.QtCore import Qt
import sys, csv, datetime
from datetime import date
"""
PyMeetings, a simple Python App using PyQt5 GUI
Records attendance and sends the resulting file to the listed person below
Version 0.45.0
CREATED BY ASHLEY DODSON
August 2017
Future improvements: Change layout to QForm/QGrid, so that resizing works
"""
class Attend(QWidget):
def __init__(self):
super().__init__()
self.initUI()
# Really need to move the PyQt5 UI off absolute and allow for input on users
# CB objects are the checkboxes. Lbl are labels for text
def initUI(self):
# Declare variables- attached to the 'self' object
self.version = "0.45.0"
self.missing = 0
self.xloc = 20
self.yloc = 15
self.submitted = False
self.users = {}
self.total_users = {}
# Rows for users
list_of_users = "" # Insert csv file with all users listed. Use, "Users" as header
self.populated_list = csv.DictReader(open(list_of_users))
for all in self.populated_list:
self.total_users[all['Users']] = all['Users']
# These for loops populate the users, but will be merged into a single for loop in the future
for each in self.total_users:
self.users[each] = QCheckBox(each, self)
self.users[each].move(self.yloc, self.xloc)
self.users[each].toggle()
self.users[each].stateChanged.connect(self.query)
self.users[each].setToolTip("Checked box means present, unchecked means absent")
self.xloc = self.xloc + 20
if (self.xloc % 160 == 0):
self.xloc = 20
self.yloc = self.yloc + 110
# Lays out basic UI
self.bckgrd = QLabel(self)
picture = QPixmap("") # Insert logo here for team or company
self.bckgrd.setPixmap(picture)
self.bckgrd.move(15, 180)
self.bckgrd.setFixedWidth(250)
self.bckgrd2 = QLabel(self)
picture2 = QPixmap("Help_Icon.png")
self.bckgrd2.setPixmap(picture2)
self.bckgrd2.move(250, 228)
self.bckgrd2.setFixedWidth(75)
self.bckgrd2.setToolTip("Version " + self.version + " created by Ashley Dodson")
self.lbl = QLabel("Total Number of missing persons is " + str(self.missing) + ".", self)
self.lbl.move(235, 212)
self.lbl.setFixedWidth(200)
self.lbl2 = QLabel("<b>Attendees</b>", self)
self.lbl2.move(75, 5)
self.lbl3 = QLabel("", self)
self.lbl3.move(257, 260)
self.lbl3.setFixedWidth(200)
submitButton = QPushButton('Submit', self)
submitButton.setToolTip("This will generate attendance and send the report to named person")
submitButton.move(335, 233)
submitButton.clicked.connect(self.submit)
pal = QPalette()
pal.setColor(QPalette.Background, Qt.white)
self.setWindowIcon(QIcon('Pymeeting_Icon.png'))
self.setAutoFillBackground(True)
self.setPalette(pal)
self.setGeometry(350, 350, 425, 300)
self.setWindowTitle('IT Meeting Attendance')
self.show()
# This part just keeps up with the number of missing employess via the variable missing
def query(self, state):
if state == Qt.Checked:
self.missing = self.missing - 1
self.lbl.setText("Total Number of missing persons is " + str(self.missing) + ".")
else:
self.missing = self.missing + 1
self.lbl.setText("Total Number of missing persons is " + str(self.missing) + ".")
# This is the button that replaces or creates a file with the attendance generated. Powershell does the transmitting
def submit(self):
self.submitted = True
fileName = "IT_Meeting.txt"
fileLocation = "" #Insert location of file here, doesn't need filename
file = open(fileLocation + fileName, "w")
file.write("IT Meeting: " + str(date.today()) + "\n")
for each in self.total_users:
if (self.users[each].checkState() == 2):
present = "Here"
else:
present = "Absent"
file.write("" + each + " is: " + present + "\n")
file.close()
self.now = datetime.datetime.now()
self.lbl3.setText("<i>Submitted " + str(self.now.strftime("%m-%d-%Y %H:%M:%S")) + "</i>")
# This is kind of what actually launches the program
if __name__ == '__main__':
app = QApplication(sys.argv)
ex = Attend()
sys.exit(app.exec_())
| [
"noreply@github.com"
] | noreply@github.com |
50ffc9ad26e7630be11c94ac91832bb11949eb9f | 40020805c65b2ff7d27bf86dd1bb79dc8a263784 | /LearningDemo/decTree/hyjia/cn/demo1.py | 168328e343ffbbe46a99cb65e412ae0d5f19a348 | [] | no_license | jhy616415267/MachineLearningDemo | 7fd4f37690f6ec42769a27ca6dac918413a5acaa | 659f606b14bf435c11aae308cce3e7e8977de60c | refs/heads/master | 2021-04-12T04:44:20.124453 | 2018-04-04T10:18:20 | 2018-04-04T10:18:20 | 125,847,725 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,625 | py | # _*_ coding: UTF-8 _*_
from math import log
def createDataSet():
dataSet = [[0, 0, 0, 0, 'no'], # 数据集
[0, 0, 0, 1, 'no'],
[0, 1, 0, 1, 'yes'],
[0, 1, 1, 0, 'yes'],
[0, 0, 0, 0, 'no'],
[1, 0, 0, 0, 'no'],
[1, 0, 0, 1, 'no'],
[1, 1, 1, 1, 'yes'],
[1, 0, 1, 2, 'yes'],
[1, 0, 1, 2, 'yes'],
[2, 0, 1, 2, 'yes'],
[2, 0, 1, 1, 'yes'],
[2, 1, 0, 1, 'yes'],
[2, 1, 0, 2, 'yes'],
[2, 0, 0, 0, 'no']]
labels = ['不放贷', '放贷'] # 分类属性
return dataSet, labels # 返回数据集和分类属性
def calcShannonEnt(dataSet):
numEntrires = len(dataSet)
labelCounts = {}
for featVec in dataSet:
currentLable = featVec[-1]
if currentLable not in labelCounts:
labelCounts[currentLable] = 0
labelCounts[currentLable] += 1
shannonEnt = 0.0
for key in labelCounts:
prob = float(labelCounts[key]) / numEntrires
shannonEnt -= prob * log(prob, 2)
return shannonEnt
# 计算指定特征的信息增益,信息增益越大则说明特征越好
def spilitdataSet(dataSet,axis,value):
retDataSet = []
for featVec in dataSet:
if featVec[axis] == value:
reducedFeatVec = featVec[:axis]
reducedFeatVec.extend(featVec[axis+1:])
retDataSet.append(reducedFeatVec)
return retDataSet
# 选择最有特征值
def chooseBestFeatureToSplit(dataSet):
numFeatures = len(dataSet[0]) -1
baseEntropy = calcShannonEnt(dataSet)
bestInfoGain = 0.0
bestFeature = -1
for i in range(numFeatures):
featList = [example[i] for example in dataSet]
uniqueVals = set(featList)
newEntropy = 0.0
for value in uniqueVals:
subDataSet = spilitdataSet(dataSet,i,value)
prob = len(subDataSet) /float(len(dataSet))
newEntropy += prob* calcShannonEnt(subDataSet)
infoGain = baseEntropy -newEntropy
print("第%d个特征的增益为%.3f" % (i, infoGain)) #打印每个特征的信息增益
if (infoGain > bestInfoGain): #计算信息增益
bestInfoGain = infoGain #更新信息增益，找到最大的信息增益
bestFeature = i
return bestFeature
if __name__ == '__main__':
dataSet, features = createDataSet()
print("最优特征索引值:" + str(chooseBestFeatureToSplit(dataSet)))
| [
"jiahongying@baidu.com"
] | jiahongying@baidu.com |
e3dfa7a4455e0f59230ecf1846ccc32cc4122744 | e6c65e2e354336a4bea5b6a4ccbccd3682915fe2 | /out-bin/py/google/fhir/models/run_locally.runfiles/com_google_fhir/external/pypi__tensorflow_1_12_0/tensorflow-1.12.0.data/purelib/tensorflow/contrib/receptive_field/python/__init__.py | 3d7249a31712e439c472e3403dd11bd766b47d41 | [
"Apache-2.0"
] | permissive | rasalt/fhir-datalab | c30ab773d84983dd04a37e9d0ddec8bf2824b8a4 | 3e329fc8b4226d3e3a4a7c23c306a86e7a9ea0de | refs/heads/master | 2021-10-09T05:51:04.593416 | 2018-12-21T18:11:03 | 2018-12-22T05:38:32 | 162,744,237 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 194 | py | /home/rkharwar/.cache/bazel/_bazel_rkharwar/0ddaa3627472ad9d1367a008236ce2f5/external/pypi__tensorflow_1_12_0/tensorflow-1.12.0.data/purelib/tensorflow/contrib/receptive_field/python/__init__.py | [
"ruchika.kharwar@gmail.com"
] | ruchika.kharwar@gmail.com |
36a9d6ed2ada559682bb6025a07002cb25ffa297 | 913c6914400f7694c694f96ad6fdc90ff5436aa6 | /A.py | e5891ac5a50d16496154cd565f1cc6f0353f0f28 | [] | no_license | konamilk/atcoder-abc187 | 128cb6e6e4cc44e6a3f63c78ce07aab03219205c | 823a6748e38b242366d27dd2e534daa98de107b4 | refs/heads/main | 2023-02-21T18:56:46.751137 | 2021-01-24T01:12:38 | 2021-01-24T01:12:38 | 330,582,867 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 227 | py | A, B = map(int, input().split())
def sumOfDigits(x: int):
sum = 0
while True:
sum += x % 10
x = x // 10
if x == 0:
break
return sum
print(max(sumOfDigits(A), sumOfDigits(B)))
| [
"hina.no.atelier@gmail.com"
] | hina.no.atelier@gmail.com |
6475c7715d2ace925da77b437721147f76ea65b2 | d1d79d0c3889316b298852834b346d4246825e66 | /blackbot/core/wss/ttp/art/art_T1218.011-1.py | ae3f24301b8519e5fd2ff913044f9b62c72f2ce2 | [] | no_license | ammasajan/Atomic-Red-Team-Intelligence-C2 | 78d1ed2de49af71d4c3c74db484e63c7e093809f | 5919804f0bdeb15ea724cd32a48f377bce208277 | refs/heads/master | 2023-07-17T12:48:15.249921 | 2021-08-21T20:10:30 | 2021-08-21T20:10:30 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,920 | py | from blackbot.core.utils import get_path_in_package
from blackbot.core.wss.atomic import Atomic
from terminaltables import SingleTable
import os
import json
class Atomic(Atomic):
def __init__(self):
self.name = 'DefenseEvasion/T1218.011-1'
self.controller_type = ''
self.external_id = 'T1218.011'
self.blackbot_id = 'T1218.011-1'
self.version = ''
self.language = 'boo'
self.description = self.get_description()
self.last_updated_by = 'Blackbot, Inc. All Rights reserved'
self.references = ["System.Management.Automation"]
self.options = {}
def payload(self):
with open(get_path_in_package('core/wss/ttp/art/src/cmd_prompt.boo'), 'r') as ttp_src:
src = ttp_src.read()
cmd_script = get_path_in_package('core/wss/ttp/art/cmd_ttp/defenseEvasion/T1218.011-1')
with open(cmd_script) as cmd:
src = src.replace("CMD_SCRIPT", cmd.read())
return src
def get_description(self):
path = get_path_in_package('core/wss/ttp/art/cmd_ttp/defenseEvasion/T1218.011-1')
with open(path) as text:
head = [next(text) for l in range(4)]
technique_name = head[0].replace('#TechniqueName: ', '').strip('\n')
atomic_name = head[1].replace('#AtomicTestName: ', '').strip('\n')
description = head[2].replace('#Description: ', '').strip('\n')
language = head[3].replace('#Language: ', '').strip('\n')
aux = ''
count = 1
for char in description:
if char == '&':
continue
aux += char
if count % 126 == 0:
aux += '\n'
count += 1
out = '{}: {}\n{}\n\n{}\n'.format(technique_name, language, atomic_name, aux)
return out
| [
"root@uw2artic201.blackbot.net"
] | root@uw2artic201.blackbot.net |
7f795bd8e63757d315da20e4371739db3358544e | dc98d2294dc3a02cdb79f606aa02bad92d6f7a46 | /numpy/numpy.py | 85b6409401a536fbe1291dd60e7b2ca7f285902c | [] | no_license | sawrupesh04/HackerRank-Python | c5d956509f03b3ed1e4852c6b63285007c6e33ad | 7979f7e7f06611ead4dd07e932e4a90503f509f2 | refs/heads/master | 2020-03-18T07:43:36.398988 | 2019-12-25T10:33:25 | 2019-12-25T10:33:25 | 134,469,852 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 898 | py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 3 10:38:26 2019
@author: rupesh
"""
import numpy
nm_inputs = list(map(int, input().split()))
arr = [list(map(int, input().split())) for _ in range(nm_inputs[0])]
arr = numpy.array(arr)
print(numpy.transpose(arr))
print(arr.flatten())
#################################################################
nmp_inputs = list(map(int, input().split()))
n = numpy.array([list(map(int, input().split())) for _ in range(nmp_inputs[0])])
m = numpy.array([list(map(int, input().split())) for _ in range(nmp_inputs[1])])
numpy.concatenate((n, m), axis=0)
##################################################################
shape = tuple(map(int, input().split()))
print(numpy.zeros(shape, dtype='int'))
print(numpy.ones(shape, dtype='int'))
#################################################################
numpy.eye(3, 3)
| [
"rupeshsaw786@gmail.com"
] | rupeshsaw786@gmail.com |
1c0a86390d4b3ad61125d12e016f641e876e5580 | 8a0a4aa84cc3796c238c26fce0ce96486a8054c8 | /mmdet/models/detectors/two_stage_panotic.py | caed364b7d41b3570f3ac31e24a461ac9d806d6c | [
"Apache-2.0"
] | permissive | Davis-love-AI/EasyPSNet | cdd53a78af9192cbfc9f2a982943daa11027e99f | 5905a9adab15cd5937a1821a67cda1813175a528 | refs/heads/master | 2022-01-27T06:55:39.802984 | 2019-06-01T08:20:12 | 2019-06-01T08:20:12 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 8,655 | py | import torch
import torch.nn as nn
from .base import BaseDetector
from .test_mixins import RPNTestMixin, BBoxTestMixin, MaskTestMixin, SegTestMixin
from .. import builder
from ..registry import DETECTORS
from mmdet.core import bbox2roi, bbox2result, build_assigner, build_sampler
@DETECTORS.register_module
class TwoStagePanoticDetector(BaseDetector, RPNTestMixin, BBoxTestMixin,
MaskTestMixin, SegTestMixin):
def __init__(self,
backbone,
neck=None,
rpn_head=None,
bbox_roi_extractor=None,
bbox_head=None,
mask_roi_extractor=None,
mask_head=None,
seg_head=None,
train_cfg=None,
test_cfg=None,
pretrained=None):
super(TwoStagePanoticDetector, self).__init__()
self.backbone = builder.build_backbone(backbone)
if neck is not None:
self.neck = builder.build_neck(neck)
else:
raise NotImplementedError
if rpn_head is not None:
self.rpn_head = builder.build_head(rpn_head)
if bbox_head is not None:
self.bbox_roi_extractor = builder.build_roi_extractor(
bbox_roi_extractor)
self.bbox_head = builder.build_head(bbox_head)
if mask_head is not None:
self.mask_roi_extractor = builder.build_roi_extractor(
mask_roi_extractor)
self.mask_head = builder.build_head(mask_head)
if seg_head is not None:
self.mask_roi_extractor = builder.build_roi_extractor(
mask_roi_extractor)
self.seg_head = builder.build_head(seg_head)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.init_weights(pretrained=pretrained)
@property
def with_rpn(self):
return hasattr(self, 'rpn_head') and self.rpn_head is not None
def init_weights(self, pretrained=None):
super(TwoStagePanoticDetector, self).init_weights(pretrained)
self.backbone.init_weights(pretrained=pretrained)
if self.with_neck:
if isinstance(self.neck, nn.Sequential):
for m in self.neck:
m.init_weights()
else:
self.neck.init_weights()
if self.with_rpn:
self.rpn_head.init_weights()
if self.with_bbox:
self.bbox_roi_extractor.init_weights()
self.bbox_head.init_weights()
if self.with_mask:
self.mask_roi_extractor.init_weights()
self.mask_head.init_weights()
def extract_feat(self, img):
x = self.backbone(img)
if self.with_neck:
x = self.neck(x)
return x
def forward_train(self,
img,
img_meta,
gt_bboxes,
gt_labels,
gt_bboxes_ignore=None,
gt_masks=None,
proposals=None,
gt_seg=None):
x = self.extract_feat(img)
losses = dict()
# RPN forward and loss
if self.with_rpn:
rpn_outs = self.rpn_head(x)
rpn_loss_inputs = rpn_outs + (gt_bboxes, img_meta,
self.train_cfg.rpn)
rpn_losses = self.rpn_head.loss(
*rpn_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
losses.update(rpn_losses)
proposal_inputs = rpn_outs + (img_meta, self.test_cfg.rpn)
proposal_list = self.rpn_head.get_bboxes(*proposal_inputs)
else:
proposal_list = proposals
# assign gts and sample proposals
if self.with_bbox or self.with_mask:
bbox_assigner = build_assigner(self.train_cfg.rcnn.assigner)
bbox_sampler = build_sampler(
self.train_cfg.rcnn.sampler, context=self)
num_imgs = img.size(0)
if gt_bboxes_ignore is None:
gt_bboxes_ignore = [None for _ in range(num_imgs)]
sampling_results = []
for i in range(num_imgs):
assign_result = bbox_assigner.assign(
proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i],
gt_labels[i])
sampling_result = bbox_sampler.sample(
assign_result,
proposal_list[i],
gt_bboxes[i],
gt_labels[i],
feats=[lvl_feat[i][None] for lvl_feat in x])
sampling_results.append(sampling_result)
# bbox head forward and loss
if self.with_bbox:
rois = bbox2roi([res.bboxes for res in sampling_results])
# TODO: a more flexible way to decide which feature maps to use
bbox_feats = self.bbox_roi_extractor(
x[:self.bbox_roi_extractor.num_inputs], rois)
cls_score, bbox_pred = self.bbox_head(bbox_feats)
bbox_targets = self.bbox_head.get_target(
sampling_results, gt_bboxes, gt_labels, self.train_cfg.rcnn)
loss_bbox = self.bbox_head.loss(cls_score, bbox_pred,
*bbox_targets)
losses.update(loss_bbox)
# mask head forward and loss
if self.with_mask:
pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
mask_feats = self.mask_roi_extractor(
x[:self.mask_roi_extractor.num_inputs], pos_rois)
mask_pred = self.mask_head(mask_feats)
mask_targets = self.mask_head.get_target(
sampling_results, gt_masks, self.train_cfg.rcnn)
pos_labels = torch.cat(
[res.pos_gt_labels for res in sampling_results])
loss_mask = self.mask_head.loss(mask_pred, mask_targets,
pos_labels)
losses.update(loss_mask)
# seg head forward and loss
seg_pred=self.seg_head(x[:self.mask_roi_extractor.num_inputs])
loss_seg = self.seg_head.loss(seg_pred, gt_seg)
losses.update(loss_seg)
return losses
def simple_test(self, img, img_meta, proposals=None, rescale=False):
"""Test without augmentation."""
assert self.with_bbox, "Bbox head must be implemented."
x = self.extract_feat(img)
proposal_list = self.simple_test_rpn(
x, img_meta, self.test_cfg.rpn) if proposals is None else proposals
det_bboxes, det_labels = self.simple_test_bboxes(
x, img_meta, proposal_list, self.test_cfg.rcnn, rescale=rescale)
bbox_results = bbox2result(det_bboxes, det_labels,
self.bbox_head.num_classes)
if not self.with_mask:
return bbox_results
else:
segm_results = self.simple_test_mask(
x, img_meta, det_bboxes, det_labels, rescale=rescale)
seg_results = self.simple_test_seg(
x[:self.mask_roi_extractor.num_inputs],img_meta, rescale=rescale)
# panoptic_results = self.simple_test_panoptic(
# segm_results, seg_results, img_meta, rescale=rescale)
return bbox_results, segm_results, seg_results
def aug_test(self, imgs, img_metas, rescale=False):
"""Test with augmentations.
If rescale is False, then returned bboxes and masks will fit the scale
of imgs[0].
"""
# recompute feats to save memory
proposal_list = self.aug_test_rpn(
self.extract_feats(imgs), img_metas, self.test_cfg.rpn)
det_bboxes, det_labels = self.aug_test_bboxes(
self.extract_feats(imgs), img_metas, proposal_list,
self.test_cfg.rcnn)
if rescale:
_det_bboxes = det_bboxes
else:
_det_bboxes = det_bboxes.clone()
_det_bboxes[:, :4] *= img_metas[0][0]['scale_factor']
bbox_results = bbox2result(_det_bboxes, det_labels,
self.bbox_head.num_classes)
# det_bboxes always keep the original scale
if self.with_mask:
segm_results = self.aug_test_mask(
self.extract_feats(imgs), img_metas, det_bboxes, det_labels)
seg_results = self.aug_test_seg(
self.extract_feats(imgs), img_metas)
return bbox_results, segm_results, seg_results
else:
return bbox_results
| [
"237942920@qq.com"
] | 237942920@qq.com |
efb43fd0f078e074941e4834234fd4d5fc9c8d22 | 6b66235b6d470aefe02418479819213e79059e9e | /code_attempt3/sandbox_deleteme.py | 96b926cda22a8a7e14210cf62484769be117d643 | [] | no_license | Sounzgudlowd1/AML_Project | 18bb286bdca0e602cbcb8d8725a8955800a825eb | 1aa29f732728a19cc2632a5d817675ce7fca6644 | refs/heads/master | 2021-04-28T16:00:20.989193 | 2018-03-29T20:47:16 | 2018-03-29T20:47:16 | 122,003,418 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 496 | py | # -*- coding: utf-8 -*-
"""
Created on Sat Mar 3 14:11:12 2018
@author: Erik
"""
import gradient_calculation as gc
import get_data as gd
import numpy as np
from scipy.optimize import check_grad
import part2b_code as p2b
import time
params = gd.get_params()
X, y = gd.read_data_formatted('train_struct.txt')
X = X[0:100]
y = y[0:100]
start = time.time()
print(check_grad(p2b.func_to_minimize, p2b.grad_func, params, X, y, 10))
print("Finished in " + str(time.time() - start) + " seconds")
| [
"eoslan2@uic.edu"
] | eoslan2@uic.edu |
a4fa98e0d9ffca78a5aaadefb03a15b8440eeefb | f3963ec76dfb97652445a471c9b583126bdd0f01 | /lstm_model.py | 54fe0a1af0d2c051074dc5f271c663105dc2edee | [
"MIT"
] | permissive | guilherme-pombo/PhilosophyLSTM | a145779080da7c207a9cf863b87d57dc8ed4fa2f | d86cf3a8c85acae9a391065d68d131d5d8beee77 | refs/heads/master | 2021-01-12T05:08:24.677773 | 2017-01-02T22:47:22 | 2017-01-02T22:47:22 | 77,863,899 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,781 | py | from __future__ import print_function
from keras.models import Graph
from keras.layers.core import Dense, Activation, Dropout, TimeDistributedDense
from keras.layers.advanced_activations import ELU
from keras.layers.recurrent import LSTM
from keras.layers.normalization import BatchNormalization
"""
LSTM params
Structure taken from RoboTrump LSTM repo
"""
input_dim = 300
lstm_hdim = 500
bridge_dim = 1000
dense_dim = 1500
sd_len = 12
batch_size = 256
layerNames = ['tdd1', 'bn1', 'lstm1', 'bn2', 'lstm2', 'bn3', 'lstm3', 'bn4', 'dropout1', 'dense1', 'denseelu1',
'dropout2', 'dense2','densesm1']
def create_model(word_coding):
"""
Create the LSTM model
:param word_coding:
:return:
"""
model = Graph()
model.add_input(name='input', input_shape=(sd_len, input_dim))
model.add_node(TimeDistributedDense(input_dim=input_dim, output_dim=lstm_hdim, input_length=sd_len),
name=layerNames[0], input='input')
model.add_node(BatchNormalization(), name=layerNames[1], input=layerNames[0])
model.add_node(LSTM(input_dim=lstm_hdim, output_dim=lstm_hdim, return_sequences=True), name=layerNames[2] + 'left',
input=layerNames[1])
model.add_node(BatchNormalization(), name=layerNames[3] + 'left', input=layerNames[2] + 'left')
model.add_node(LSTM(input_dim=lstm_hdim, output_dim=lstm_hdim, return_sequences=True, go_backwards=True),
name=layerNames[2] + 'right', input=layerNames[1])
model.add_node(BatchNormalization(), name=layerNames[3] + 'right', input=layerNames[2] + 'right')
model.add_node(LSTM(input_dim=lstm_hdim, output_dim=lstm_hdim, return_sequences=False), name=layerNames[6] + 'left',
input=layerNames[3] + 'left')
model.add_node(LSTM(input_dim=lstm_hdim, output_dim=lstm_hdim, return_sequences=False, go_backwards=True),
name=layerNames[6] + 'right', input=layerNames[3] + 'right')
model.add_node(BatchNormalization(), name=layerNames[7], inputs=[layerNames[6] + 'left', layerNames[6] + 'right'])
model.add_node(Dropout(0.2), name=layerNames[8], input=layerNames[7])
model.add_node(Dense(input_dim=bridge_dim, output_dim=dense_dim), name=layerNames[9], input=layerNames[8])
model.add_node(ELU(), name=layerNames[10], input=layerNames[9])
model.add_node(Dropout(0.2), name=layerNames[11], input=layerNames[10])
model.add_node(Dense(input_dim=dense_dim, output_dim=len(word_coding)), name=layerNames[12], input=layerNames[11])
model.add_node(Activation('softmax'), name=layerNames[13], input=layerNames[12])
model.add_output(name='output1', input=layerNames[13])
model.compile(optimizer='rmsprop', loss={'output1': 'categorical_crossentropy'})
return model
| [
"guilherme@brandwatch.com"
] | guilherme@brandwatch.com |
34595e216c689a3d727e2ac979dcd348526a6ab3 | 2eb33c65c3d9eef6d7ad974214c4dae705921fb3 | /code/depthest_trainer.py | fbce91d8009026c2ad7b6fcf44d98f403c957e6e | [
"MIT"
] | permissive | inyong37/ACAN | 08022118820c29d847b40ac182c8680a2d33f9fc | 0847c14cb49e2b53ae6486f62cb0f57efd2123c2 | refs/heads/master | 2020-09-13T19:25:54.084780 | 2019-08-21T11:36:31 | 2019-08-21T11:36:31 | 222,881,603 | 1 | 0 | MIT | 2019-11-20T07:55:31 | 2019-11-20T07:55:30 | null | UTF-8 | Python | false | false | 12,764 | py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: chenyuru
## This source code is licensed under the MIT-style license
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
import os
import sys
import time
import datetime
import numpy as np
import scipy.io
import shutil
from tensorboardX import SummaryWriter
from trainers import Trainer, DataPrefetcher
from utils import predict_multi_scale, predict_whole_img, compute_errors, \
display_figure, colored_depthmap, merge_images
import torch
from torch.nn import DataParallel
import matplotlib.pyplot as plt
from tqdm import tqdm
from copy import deepcopy
import json
class DepthEstimationTrainer(Trainer):
def __init__(self, params, net, datasets, criterion, optimizer, scheduler,
sets=['train', 'val', 'test'], verbose=100, stat=False,
eval_func=compute_errors,
disp_func=display_figure):
self.time = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
self.params = params
self.verbose = verbose
self.eval_func = eval_func
self.disp_func = disp_func
# Init dir
if params.workdir is not None:
workdir = os.path.expanduser(params.workdir)
if params.logdir is None:
logdir = os.path.join(workdir, 'log_{}_{}'.format(params.encoder+params.decoder, params.dataset))
else:
logdir = os.path.join(workdir, params.logdir)
resdir = None
if self.params.mode == 'test':
if params.resdir is None:
resdir = os.path.join(logdir, 'res')
else:
resdir = os.path.join(logdir, params.resdir)
# Call the constructor of the parent class (Trainer)
super().__init__(net, datasets, optimizer, scheduler, criterion,
batch_size=params.batch, batch_size_val=params.batch_val,
max_epochs=params.epochs, threads=params.threads, eval_freq=params.eval_freq,
use_gpu=params.gpu, resume=params.resume, mode=params.mode,
sets=sets, workdir=workdir, logdir=logdir, resdir=resdir)
self.params.logdir = self.logdir
self.params.resdir = self.resdir
# params json
if self.params.mode == 'train':
with open(os.path.join(self.logdir, 'params_{}.json'.format(self.time)), 'w') as f:
json.dump(vars(self.params), f)
# uncomment to display the model complexity
if stat:
from torchstat import stat
import tensorwatch as tw
#net_copy = deepcopy(self.net)
stat(self.net, (3, *self.datasets[sets[0]].input_size))
exit()
tw.draw_model(self.net, (1, 3, *self.datasets[sets[0]].input_size))
#del net_copy
self.print('###### Experiment Parameters ######')
for k, v in vars(self.params).items():
self.print('{0:<22s} : {1:}'.format(k, v))
def train(self):
torch.backends.cudnn.benchmark = True
if self.logdir:
self.writer = SummaryWriter(self.logdir)
else:
raise Exception("Log dir doesn't exist!")
# Calculate total step
self.n_train = len(self.trainset)
self.steps_per_epoch = np.ceil(self.n_train / self.batch_size).astype(np.int32)
self.verbose = min(self.verbose, self.steps_per_epoch)
self.n_steps = self.max_epochs * self.steps_per_epoch
self.print("{0:<22s} : {1:} ".format('trainset sample', self.n_train))
# calculate model parameters memory
para = sum([np.prod(list(p.size())) for p in self.net.parameters()])
memory = para * 4 / (1024**2)
self.print('Model {} : params: {:,}, Memory {:.3f}MB'.format(self.net._get_name(), para, memory))
# GO!!!!!!!!!
start_time = time.time()
self.train_total_time = 0
self.time_sofar = 0
for epoch in range(self.start_epoch, self.max_epochs + 1):
# Train one epoch
total_loss = self.train_epoch(epoch)
torch.cuda.empty_cache()
# Decay Learning Rate
if self.params.scheduler in ['step', 'plateau']:
self.scheduler.step()
# Evaluate the model
if self.eval_freq and epoch % self.eval_freq == 0:
measures = self.eval(epoch)
torch.cuda.empty_cache()
for k in sorted(list(measures.keys())):
self.writer.add_scalar(k, measures[k], epoch)
self.print("Finished training! Best epoch {} best acc {:.4f}".format(self.best_epoch, self.best_acc))
self.print("Spend time: {:.2f}h".format((time.time() - start_time) / 3600))
net_type = type(self.net).__name__
best_pkl = os.path.join(self.logdir, '{}_{:03d}.pkl'.format(net_type, self.best_epoch))
modify = os.path.join(self.logdir, 'best.pkl')
shutil.copyfile(best_pkl, modify)
return
def train_epoch(self, epoch):
self.net.train()
device = torch.device('cuda:0' if self.use_gpu else 'cpu')
self.net.to(device)
self.criterion.to(device)
# Iterate over data.
prefetcher = DataPrefetcher(self.trainloader)
data = prefetcher.next()
step = 0
while data is not None:
images, labels = data[0].to(device), data[1].to(device)
before_op_time = time.time()
self.optimizer.zero_grad()
output = self.net(images)
loss1, loss2, loss3, total_loss = self.criterion(output, labels, epoch)
total_loss.backward()
self.optimizer.step()
fps = images.shape[0] / (time.time() - before_op_time)
time_sofar = self.train_total_time / 3600
time_left = (self.n_steps / self.global_step - 1.0) * time_sofar
lr = self.optimizer.param_groups[0]['lr']
if self.verbose > 0 and (step + 1) % (self.steps_per_epoch // self.verbose) == 0:
if self.params.classifier == 'OHEM':
ratio = self.criterion.AppearanceLoss.ohem_ratio
print_str = 'Epoch[{:>2}/{:>2}] | Step[{:>4}/{:>4}] | fps {:4.2f} | Loss1 {:7.3f} | Loss2 {:7.3f} | Loss3 {:7.3f} | elapsed {:.2f}h | left {:.2f}h | OHEM {:.4f} | lr {:.3e}'. \
format(epoch, self.max_epochs, step + 1, self.steps_per_epoch, fps, loss1, loss2, loss3, time_sofar, time_left, ratio, lr)
self.writer.add_scalar('OHEM', ratio)
else:
print_str = 'Epoch[{:>2}/{:>2}] | Step[{:>4}/{:>4}] | fps {:4.2f} | Loss1 {:7.3f} | Loss2 {:7.3f} | Loss3 {:7.3f} | elapsed {:.2f}h | left {:.2f}h | lr {:.3e}'. \
format(epoch, self.max_epochs, step + 1, self.steps_per_epoch, fps, loss1, loss2, loss3, time_sofar, time_left, lr)
self.print(print_str)
self.writer.add_scalar('loss1', loss1, self.global_step)
self.writer.add_scalar('loss2', loss2, self.global_step)
self.writer.add_scalar('loss3', loss3, self.global_step)
self.writer.add_scalar('total_loss', total_loss, self.global_step)
self.writer.add_scalar('lr', lr, epoch)
# Decay Learning Rate
if self.params.scheduler == 'poly':
self.scheduler.step()
self.global_step += 1
self.train_total_time += time.time() - before_op_time
data = prefetcher.next()
step += 1
return total_loss
def eval(self, epoch):
torch.backends.cudnn.benchmark = True
self.n_val = len(self.valset)
self.print("{0:<22s} : {1:} ".format('valset sample', self.n_val))
self.print("<-------------Evaluate the model-------------->")
# Evaluate one epoch
measures, fps = self.eval_epoch(epoch)
measures = {key: round(value/self.n_val, 5) for key, value in measures.items()}
acc = measures['a1']
self.print('The {}th epoch, fps {:4.2f} | {}'.format(epoch, fps, measures))
# Save the checkpoint
if self.logdir:
self.save(epoch, acc)
else:
if acc >= self.best_acc:
self.best_epoch, self.acc = epoch, acc
return measures
def eval_epoch(self, epoch):
device = torch.device('cuda:0' if self.use_gpu else 'cpu')
self.net.to(device)
self.criterion.to(device)
self.net.eval()
val_total_time = 0
measure_list = ['a1', 'a2', 'a3', 'rmse', 'rmse_log', 'log10', 'abs_rel', 'sq_rel']
measures = dict(zip(measure_list, np.zeros(len(measure_list))))
with torch.no_grad():
sys.stdout.flush()
tbar = tqdm(self.valloader)
for step, data in enumerate(self.valloader):
images, labels = data[0].to(device), data[1].to(device)
# forward
before_op_time = time.time()
output = self.net(images)
depths = self.net.inference(output)
duration = time.time() - before_op_time
val_total_time += duration
# accuracy
new = self.eval_func(labels, depths)
for i in range(len(measure_list)):
measures[measure_list[i]] += new[measure_list[i]].item()
# display images
if step == 10 and self.disp_func is not None:
self.disp_func(self.params, self.writer, self.net, images, labels, depths, epoch)
print_str = 'Test step [{}/{}].'.format(step + 1, len(self.valloader))
tbar.set_description(print_str)
fps = self.n_val / val_total_time
return measures, fps
def test(self):
n_test = len(self.testset)
for k, v in vars(self.params).items():
self.print('{0:<22s} : {1:}'.format(k, v))
self.print("{0:<22s} : {1:} ".format('testset sample', n_test))
device = torch.device('cuda:0' if self.use_gpu else 'cpu')
self.net.to(device)
self.net.eval()
self.print("<-------------Test the model-------------->")
colormaps = {'nyu': plt.cm.jet, 'kitti': plt.cm.plasma}
cm = colormaps[self.params.dataset]
measure_list = ['a1', 'a2', 'a3', 'rmse', 'rmse_log', 'log10', 'abs_rel', 'sq_rel']
measures = {key: 0 for key in measure_list}
test_total_time = 0
with torch.no_grad():
#sys.stdout.flush()
#tbar = tqdm(self.testloader)
for step, data in enumerate(self.testloader):
images, labels = data[0].to(device), data[1].to(device)
before_op_time = time.time()
if self.params.use_ms:
depths = predict_multi_scale(self.net, images, ([0.75, 1, 1.25]),
self.params.classes, self.params.use_flip)
else:
depths = predict_multi_scale(self.net, images, [1],
self.params.classes, self.params.use_flip)
duration = time.time() - before_op_time
test_total_time += duration
# accuracy
new = self.eval_func(labels, depths)
print_str = "Test step [{}/{}], a1: {:.5f}, rmse: {:.5f}.".format(step + 1, n_test, new['a1'], new['rmse'])
self.print(print_str)
#tbar.set_description(print_str)
#sys.stdout.flush()
images = images.cpu().numpy().squeeze().transpose(1, 2, 0)
labels = labels.cpu().numpy().squeeze()
depths = depths.cpu().numpy().squeeze()
labels = colored_depthmap(labels, cmap=cm).squeeze()
depths = colored_depthmap(depths, cmap=cm).squeeze()
#fuse = merge_images(images, labels, depths, self.params.min_depth, self.params.max_depth)
plt.imsave(os.path.join(self.resdir, '{:04}_rgb.png'.format(step)), images)
plt.imsave(os.path.join(self.resdir, '{:04}_gt.png'.format(step)), labels)
plt.imsave(os.path.join(self.resdir, '{:04}_depth.png'.format(step)), depths)
for i in range(len(measure_list)):
measures[measure_list[i]] += new[measure_list[i]].item()
fps = n_test / test_total_time
measures = {key: round(value / n_test, 5) for key, value in measures.items()}
self.print('Testing done, fps {:4.2f} | {}'.format(fps, measures))
return
if __name__ == '__main__':
pass
| [
"532891184@qq.com"
] | 532891184@qq.com |
cbe7ed221eb94f7edbdfe154ce52e551861b265e | 3f6d198376a6a48aa92fa14eec4fb2fa41178539 | /notebooks/mypysmps/util/windrose.py | 918e563bff0d5511a3954376692dceaaf3e6447d | [
"MIT"
] | permissive | fvanden/SOC-app | 9584b5ecc060ce034a71542f79118f9f2a94114a | 488322c05b6ebe646f003274fa1a2125d993ecd0 | refs/heads/master | 2023-05-07T16:28:28.840398 | 2021-06-03T09:37:20 | 2021-06-03T09:37:20 | 373,448,861 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 20,403 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
__version__ = '1.4'
__author__ = 'Lionel Roubeyrie'
__mail__ = 'lionel.roubeyrie@gmail.com'
__license__ = 'CeCILL-B'
import matplotlib
import matplotlib.cm as cm
import numpy as np
from matplotlib.patches import Rectangle, Polygon
from matplotlib.ticker import ScalarFormatter, AutoLocator
from matplotlib.text import Text, FontProperties
from matplotlib.projections.polar import PolarAxes
from numpy.lib.twodim_base import histogram2d
import matplotlib.pyplot as plt
#from pylab import poly_between
RESOLUTION = 100
ZBASE = -1000 #The starting zorder for all drawing, negative to have the grid on
class WindroseAxes(PolarAxes):
"""
Create a windrose axes
"""
def __init__(self, *args, **kwargs):
"""
See Axes base class for args and kwargs documentation
"""
#Uncomment to have the possibility to change the resolution directly
#when the instance is created
#self.RESOLUTION = kwargs.pop('resolution', 100)
PolarAxes.__init__(self, *args, **kwargs)
self.set_aspect('equal', adjustable='box', anchor='C')
self.radii_angle = 67.5
self.cla()
def cla(self):
"""
Clear the current axes
"""
PolarAxes.cla(self)
self.theta_angles = np.arange(0, 360, 45)
self.theta_labels = ['E', 'N-E', 'N', 'N-W', 'W', 'S-W', 'S', 'S-E']
self.set_thetagrids(angles=self.theta_angles, labels=self.theta_labels)
self._info = {'dir' : list(),
'bins' : list(),
'table' : list()}
self.patches_list = list()
def _colors(self, cmap, n):
'''
Returns a list of n colors based on the colormap cmap
'''
return [cmap(i) for i in np.linspace(0.0, 1.0, n)]
def set_radii_angle(self, **kwargs):
"""
Set the radii labels angle
"""
null = kwargs.pop('labels', None)
angle = kwargs.pop('angle', None)
if angle is None:
angle = self.radii_angle
self.radii_angle = angle
radii = np.linspace(0.1, self.get_rmax(), 6)
radii_labels = [ "%.1f" %r for r in radii ]
radii_labels[0] = "" #Removing label 0
null = self.set_rgrids(radii=radii, labels=radii_labels,
angle=self.radii_angle, **kwargs)
def _update(self):
self.set_rmax(rmax=np.max(np.sum(self._info['table'], axis=0)))
self.set_radii_angle(angle=self.radii_angle)
def legend(self, loc='lower left', **kwargs):
"""
Sets the legend location and her properties.
The location codes are
'best' : 0,
'upper right' : 1,
'upper left' : 2,
'lower left' : 3,
'lower right' : 4,
'right' : 5,
'center left' : 6,
'center right' : 7,
'lower center' : 8,
'upper center' : 9,
'center' : 10,
If none of these are suitable, loc can be a 2-tuple giving x,y
in axes coords, ie,
loc = (0, 1) is left top
loc = (0.5, 0.5) is center, center
and so on. The following kwargs are supported:
isaxes=True # whether this is an axes legend
prop = FontProperties(size='smaller') # the font property
pad = 0.2 # the fractional whitespace inside the legend border
shadow # if True, draw a shadow behind legend
labelsep = 0.005 # the vertical space between the legend entries
handlelen = 0.05 # the length of the legend lines
handletextsep = 0.02 # the space between the legend line and legend text
axespad = 0.02 # the border between the axes and legend edge
"""
def get_handles():
handles = list()
for p in self.patches_list:
if isinstance(p, matplotlib.patches.Polygon) or \
isinstance(p, matplotlib.patches.Rectangle):
color = p.get_facecolor()
elif isinstance(p, matplotlib.lines.Line2D):
color = p.get_color()
else:
raise AttributeError("Can't handle patches")
handles.append(Rectangle((0, 0), 0.2, 0.2,
facecolor=color, edgecolor='black'))
return handles
def get_labels():
labels = np.copy(self._info['bins'])
labels = ["[%.1f : %0.1f[" %(labels[i], labels[i+1]) \
for i in range(len(labels)-1)]
return labels
null = kwargs.pop('labels', None)
null = kwargs.pop('handles', None)
handles = get_handles()
labels = get_labels()
self.legend_ = matplotlib.legend.Legend(self, handles, labels,
loc, **kwargs)
return self.legend_
def _init_plot(self, dir, var, **kwargs):
"""
Internal method used by all plotting commands
"""
#self.cla()
null = kwargs.pop('zorder', None)
#Init of the bins array if not set
bins = kwargs.pop('bins', None)
if bins is None:
bins = np.linspace(np.min(var), np.max(var), 6)
if isinstance(bins, int):
bins = np.linspace(np.min(var), np.max(var), bins)
bins = np.asarray(bins)
nbins = len(bins)
#Number of sectors
nsector = kwargs.pop('nsector', None)
if nsector is None:
nsector = 16
#Sets the colors table based on the colormap or the "colors" argument
colors = kwargs.pop('colors', None)
cmap = kwargs.pop('cmap', None)
if colors is not None:
if isinstance(colors, str):
colors = [colors]*nbins
if isinstance(colors, (tuple, list)):
if len(colors) != nbins:
raise ValueError("colors and bins must have same length")
else:
if cmap is None:
cmap = cm.jet
colors = self._colors(cmap, nbins)
#Building the angles list
angles = np.arange(0, -2*np.pi, -2*np.pi/nsector) + np.pi/2
normed = kwargs.pop('normed', False)
blowto = kwargs.pop('blowto', False)
#Set the global information dictionnary
self._info['dir'], self._info['bins'], self._info['table'] = histogram(dir, var, bins, nsector, normed, blowto)
return bins, nbins, nsector, colors, angles, kwargs
def contour(self, dir, var, **kwargs):
"""
Plot a windrose in linear mode. For each var bins, a line will be
draw on the axes, a segment between each sector (center to center).
Each line can be formated (color, width, ...) like with standard plot
pylab command.
Mandatory:
* dir : 1D array - directions the wind blows from, North centred
* var : 1D array - values of the variable to compute. Typically the wind
speeds
Optional:
* nsector: integer - number of sectors used to compute the windrose
table. If not set, nsectors=16, then each sector will be 360/16=22.5°,
and the resulting computed table will be aligned with the cardinals
points.
* bins : 1D array or integer- number of bins, or a sequence of
bins variable. If not set, bins=6, then
bins=linspace(min(var), max(var), 6)
* blowto : bool. If True, the windrose will be pi rotated,
to show where the wind blow to (usefull for pollutant rose).
* colors : string or tuple - one string color ('k' or 'black'), in this
case all bins will be plotted in this color; a tuple of matplotlib
color args (string, float, rgb, etc), different levels will be plotted
in different colors in the order specified.
* cmap : a cm Colormap instance from matplotlib.cm.
- if cmap == None and colors == None, a default Colormap is used.
others kwargs : see help(pylab.plot)
"""
bins, nbins, nsector, colors, angles, kwargs = self._init_plot(dir, var,
**kwargs)
#closing lines
angles = np.hstack((angles, angles[-1]-2*np.pi/nsector))
vals = np.hstack((self._info['table'],
np.reshape(self._info['table'][:,0],
(self._info['table'].shape[0], 1))))
offset = 0
for i in range(nbins):
val = vals[i,:] + offset
offset += vals[i, :]
zorder = ZBASE + nbins - i
patch = self.plot(angles, val, color=colors[i], zorder=zorder,
**kwargs)
self.patches_list.extend(patch)
self._update()
def contourf(self, dir, var, **kwargs):
"""
Plot a windrose in filled mode. For each var bins, a line will be
draw on the axes, a segment between each sector (center to center).
Each line can be formated (color, width, ...) like with standard plot
pylab command.
Mandatory:
* dir : 1D array - directions the wind blows from, North centred
* var : 1D array - values of the variable to compute. Typically the wind
speeds
Optional:
* nsector: integer - number of sectors used to compute the windrose
table. If not set, nsectors=16, then each sector will be 360/16=22.5°,
and the resulting computed table will be aligned with the cardinals
points.
* bins : 1D array or integer- number of bins, or a sequence of
bins variable. If not set, bins=6, then
bins=linspace(min(var), max(var), 6)
* blowto : bool. If True, the windrose will be pi rotated,
to show where the wind blow to (usefull for pollutant rose).
* colors : string or tuple - one string color ('k' or 'black'), in this
case all bins will be plotted in this color; a tuple of matplotlib
color args (string, float, rgb, etc), different levels will be plotted
in different colors in the order specified.
* cmap : a cm Colormap instance from matplotlib.cm.
- if cmap == None and colors == None, a default Colormap is used.
others kwargs : see help(pylab.plot)
"""
bins, nbins, nsector, colors, angles, kwargs = self._init_plot(dir, var,
**kwargs)
null = kwargs.pop('facecolor', None)
null = kwargs.pop('edgecolor', None)
#closing lines
angles = np.hstack((angles, angles[-1]-2*np.pi/nsector))
vals = np.hstack((self._info['table'],
np.reshape(self._info['table'][:,0],
(self._info['table'].shape[0], 1))))
offset = 0
for i in range(nbins):
val = vals[i,:] + offset
offset += vals[i, :]
zorder = ZBASE + nbins - i
xs, ys = poly_between(angles, 0, val)
patch = self.fill(xs, ys, facecolor=colors[i],
edgecolor=colors[i], zorder=zorder, **kwargs)
self.patches_list.extend(patch)
def bar(self, dir, var, **kwargs):
"""
Plot a windrose in bar mode. For each var bins and for each sector,
a colored bar will be draw on the axes.
Mandatory:
* dir : 1D array - directions the wind blows from, North centred
* var : 1D array - values of the variable to compute. Typically the wind
speeds
Optional:
* nsector: integer - number of sectors used to compute the windrose
table. If not set, nsectors=16, then each sector will be 360/16=22.5°,
and the resulting computed table will be aligned with the cardinals
points.
* bins : 1D array or integer- number of bins, or a sequence of
bins variable. If not set, bins=6 between min(var) and max(var).
* blowto : bool. If True, the windrose will be pi rotated,
to show where the wind blow to (usefull for pollutant rose).
* colors : string or tuple - one string color ('k' or 'black'), in this
case all bins will be plotted in this color; a tuple of matplotlib
color args (string, float, rgb, etc), different levels will be plotted
in different colors in the order specified.
* cmap : a cm Colormap instance from matplotlib.cm.
- if cmap == None and colors == None, a default Colormap is used.
edgecolor : string - The string color each edge bar will be plotted.
Default : no edgecolor
* opening : float - between 0.0 and 1.0, to control the space between
each sector (1.0 for no space)
"""
bins, nbins, nsector, colors, angles, kwargs = self._init_plot(dir, var,
**kwargs)
null = kwargs.pop('facecolor', None)
edgecolor = kwargs.pop('edgecolor', None)
if edgecolor is not None:
if not isinstance(edgecolor, str):
raise ValueError('edgecolor must be a string color')
opening = kwargs.pop('opening', None)
if opening is None:
opening = 0.8
dtheta = 2*np.pi/nsector
opening = dtheta*opening
for j in range(nsector):
offset = 0
for i in range(nbins):
if i > 0:
offset += self._info['table'][i-1, j]
val = self._info['table'][i, j]
zorder = ZBASE + nbins - i
patch = Rectangle((angles[j]-opening/2, offset), opening, val,
facecolor=colors[i], edgecolor=edgecolor, zorder=zorder,
**kwargs)
self.add_patch(patch)
if j == 0:
self.patches_list.append(patch)
self._update()
def box(self, dir, var, **kwargs):
"""
Plot a windrose in proportional bar mode. For each var bins and for each
sector, a colored bar will be draw on the axes.
Mandatory:
* dir : 1D array - directions the wind blows from, North centred
* var : 1D array - values of the variable to compute. Typically the wind
speeds
Optional:
* nsector: integer - number of sectors used to compute the windrose
table. If not set, nsectors=16, then each sector will be 360/16=22.5°,
and the resulting computed table will be aligned with the cardinals
points.
* bins : 1D array or integer- number of bins, or a sequence of
bins variable. If not set, bins=6 between min(var) and max(var).
* blowto : bool. If True, the windrose will be pi rotated,
to show where the wind blow to (usefull for pollutant rose).
* colors : string or tuple - one string color ('k' or 'black'), in this
case all bins will be plotted in this color; a tuple of matplotlib
color args (string, float, rgb, etc), different levels will be plotted
in different colors in the order specified.
* cmap : a cm Colormap instance from matplotlib.cm.
- if cmap == None and colors == None, a default Colormap is used.
edgecolor : string - The string color each edge bar will be plotted.
Default : no edgecolor
"""
bins, nbins, nsector, colors, angles, kwargs = self._init_plot(dir, var,
**kwargs)
null = kwargs.pop('facecolor', None)
edgecolor = kwargs.pop('edgecolor', None)
if edgecolor is not None:
if not isinstance(edgecolor, str):
raise ValueError('edgecolor must be a string color')
opening = np.linspace(0.0, np.pi/16, nbins)
for j in range(nsector):
offset = 0
for i in range(nbins):
if i > 0:
offset += self._info['table'][i-1, j]
val = self._info['table'][i, j]
zorder = ZBASE + nbins - i
patch = Rectangle((angles[j]-opening[i]/2, offset), opening[i],
val, facecolor=colors[i], edgecolor=edgecolor,
zorder=zorder, **kwargs)
self.add_patch(patch)
if j == 0:
self.patches_list.append(patch)
self._update()
def histogram(dir, var, bins, nsector, normed=False, blowto=False):
"""
Returns an array where, for each sector of wind
(centred on the north), we have the number of time the wind comes with a
particular var (speed, polluant concentration, ...).
* dir : 1D array - directions the wind blows from, North centred
* var : 1D array - values of the variable to compute. Typically the wind
speeds
* bins : list - list of var category against we're going to compute the table
* nsector : integer - number of sectors
* normed : boolean - The resulting table is normed in percent or not.
* blowto : boolean - Normaly a windrose is computed with directions
as wind blows from. If true, the table will be reversed (usefull for
pollutantrose)
"""
if len(var) != len(dir):
raise ValueError("var and dir must have same length")
angle = 360./nsector
dir_bins = np.arange(-angle/2 ,360.+angle, angle, dtype=np.float)
dir_edges = dir_bins.tolist()
dir_edges.pop(-1)
dir_edges[0] = dir_edges.pop(-1)
dir_bins[0] = 0.
var_bins = bins.tolist()
var_bins.append(np.inf)
if blowto:
dir = dir + 180.
dir[dir>=360.] = dir[dir>=360.] - 360
table = histogram2d(x=var, y=dir, bins=[var_bins, dir_bins],
normed=False)[0]
# add the last value to the first to have the table of North winds
table[:,0] = table[:,0] + table[:,-1]
# and remove the last col
table = table[:, :-1]
if normed:
table = table*100/table.sum()
return dir_edges, var_bins, table
def wrcontour(dir, var, **kwargs):
fig = plt.figure()
rect = [0.1, 0.1, 0.8, 0.8]
ax = WindroseAxes(fig, rect)
fig.add_axes(ax)
ax.contour(dir, var, **kwargs)
l = ax.legend(axespad=-0.10)
plt.setp(l.get_texts(), fontsize=8)
plt.draw()
plt.show()
return ax
def wrcontourf(dir, var, **kwargs):
fig = plt.figure()
rect = [0.1, 0.1, 0.8, 0.8]
ax = WindroseAxes(fig, rect)
fig.add_axes(ax)
ax.contourf(dir, var, **kwargs)
l = ax.legend(axespad=-0.10)
plt.setp(l.get_texts(), fontsize=8)
plt.draw()
plt.show()
return ax
def wrbox(dir, var, **kwargs):
fig = plt.figure()
rect = [0.1, 0.1, 0.8, 0.8]
ax = WindroseAxes(fig, rect)
fig.add_axes(ax)
ax.box(dir, var, **kwargs)
l = ax.legend(axespad=-0.10)
plt.setp(l.get_texts(), fontsize=8)
plt.draw()
plt.show()
return ax
def wrbar(dir, var, **kwargs):
fig = plt.figure()
rect = [0.1, 0.1, 0.8, 0.8]
ax = WindroseAxes(fig, rect)
fig.add_axes(ax)
ax.bar(dir, var, **kwargs)
l = ax.legend(axespad=-0.10)
plt.setp(l.get_texts(), fontsize=8)
plt.draw()
plt.show()
return ax
def clean(dir, var):
'''
Remove masked values in the two arrays, where if a direction data is masked,
the var data will also be removed in the cleaning process (and vice-versa)
'''
dirmask = dir.mask==False
varmask = var.mask==False
ind = dirmask*varmask
return dir[ind], var[ind]
if __name__=='__main__':
from pylab import figure, show, setp, random, grid, draw
vv=random(500)*6
dv=random(500)*360
fig = figure(figsize=(8, 8), dpi=80, facecolor='w', edgecolor='w')
rect = [0.1, 0.1, 0.8, 0.8]
ax = WindroseAxes(fig, rect, axisbg='w')
fig.add_axes(ax)
# ax.contourf(dv, vv, bins=np.arange(0,8,1), cmap=cm.hot)
# ax.contour(dv, vv, bins=np.arange(0,8,1), colors='k')
# ax.bar(dv, vv, normed=True, opening=0.8, edgecolor='white')
ax.box(dv, vv, normed=True)
l = ax.legend(axespad=-0.10)
setp(l.get_texts(), fontsize=8)
draw()
#print ax._info
show()
| [
"floorvandenheuvel@hotmail.com"
] | floorvandenheuvel@hotmail.com |
c845b3f122e9eb3f5170029f82dc0a50326a9376 | d06def5f63992ea4ef03537fad7be8beef9f1b6c | /Code/downloadhandler.py | aead68c20469042b85b4888995401632182eeccf | [] | no_license | CiaranWinna/dropbox-replica | 8db2b69a649fcccca8175bde2f93b12274ef0abd | 1ccfbc6bd7e25c2ac42a3f428162ce44e1749024 | refs/heads/main | 2023-03-26T23:05:21.932123 | 2021-04-01T17:04:20 | 2021-04-01T17:04:20 | 353,769,917 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 811 | py | # importing the required libraries
import webapp2
import jinja2
from google.appengine.ext import ndb
from google.appengine.ext import blobstore
from google.appengine.ext.webapp import blobstore_handlers
class DownloadHandler(blobstore_handlers.BlobstoreDownloadHandler):
# model that is used when downloading a file
def get(self):
# a get method that will accept the passed index of the file to be downloaded
index = int(self.request.get('index'))
# getting the collection key
collection_key = ndb.Key('BlobCollection', 1)
# retrieving the the blobcollection object from the datastore
collection = collection_key.get()
# sending the blob with the specified location to the user
self.send_blob(collection.blobs[index])
| [
"ciaranwinnan@pop-os.localdomain"
] | ciaranwinnan@pop-os.localdomain |
62c0bb196d8d6513da33147d257fd4d507bb8ee1 | fcf63f498acc8f29c68773e9b20131bfce42f281 | /facetest.py | de125285bf47638ef5b67a409d5a381ec87e58f6 | [] | no_license | dstoner05/facial-recognition | 16758a567cc4e8774488ba7d4c0b39a4d98fe8fb | 048556db56162a42324f85a962057210f0f520dd | refs/heads/main | 2023-08-03T15:57:44.640046 | 2021-09-10T19:20:34 | 2021-09-10T19:20:34 | 401,709,908 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 710,337 | py | import requests
from PIL import Image
from numpy import asarray
import cv2
import numpy as np
import os
import base64
import io
from numpy import array
filename = "imran.jpg"
# img = cv2.imread(filename, 0)
with open(filename, "rb") as img:
string = base64.b64encode(img.read()).decode('utf-8')
# string = 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\"\r\n}"
cid = 2
def test():
api_url = "http://192.168.8.13:5000/facial-recognition"
response = requests.post(url= api_url, json={'user_photo':string, 'company_id':cid})
assert response.status_code == 200
response_data = response.json()
# print(response)
# print(response_data)
print(response_data['response'])
print(response_data['empid'])
print(response_data['error'])
# .13.81
def __main__():
test()
if __name__ == '__main__':
__main__() | [
"58181886+dstoner05@users.noreply.github.com"
] | 58181886+dstoner05@users.noreply.github.com |
3c74398fe91bbc87e237a9ef137155cef7f3ca67 | df1c621021e5448169b624a4dbea44e4dce4a0af | /test/test.py | 01a7c0e7e654eab99b253ea6e35a266a8f815b32 | [] | no_license | K4ndo/FinanceControl | 5e399c2fd28c0f212d69dd047d86ed11f27af0c9 | a18cd83adbab384908e94385fd6efe1a70c8f483 | refs/heads/master | 2020-04-09T14:32:03.812592 | 2019-06-06T18:56:53 | 2019-06-06T18:56:53 | 160,400,281 | 0 | 1 | null | 2019-06-10T11:18:55 | 2018-12-04T18:18:50 | Python | UTF-8 | Python | false | false | 27 | py | from src import dataModel
| [
"dkessler@twt-gmbh.de"
] | dkessler@twt-gmbh.de |
01e1441294cda302a160e5771d99e199e575a62e | 90cdfc6ff827c8334c81f6f896b1081cbb4d4f7a | /07GUI/08Pyqt5/06QtLearning/main.py | 67e007139e350075c02c31f2644d82b77e45fcbe | [] | no_license | HBU/Jupyter | c79883f329efd2426c5c8fde1364266ed8b5059f | b3d5d08c89c26c68027409c2b466ac64aeb1af39 | refs/heads/master | 2022-07-06T22:00:43.694050 | 2020-12-22T09:53:02 | 2020-12-22T09:53:02 | 123,717,897 | 3 | 3 | null | 2022-07-06T19:20:58 | 2018-03-03T18:04:01 | Jupyter Notebook | UTF-8 | Python | false | false | 3,190 | py | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'main.ui'
#
# Created by: PyQt5 UI code generator 5.10.1
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_Dialog(object):
def setupUi(self, Dialog):
Dialog.setObjectName("Dialog")
Dialog.resize(517, 400)
self.label = QtWidgets.QLabel(Dialog)
self.label.setGeometry(QtCore.QRect(80, 10, 211, 61))
font = QtGui.QFont()
font.setFamily("微软雅黑")
font.setPointSize(36)
self.label.setFont(font)
self.label.setObjectName("label")
self.tableView = QtWidgets.QTableView(Dialog)
self.tableView.setGeometry(QtCore.QRect(60, 100, 256, 261))
self.tableView.setObjectName("tableView")
self.layoutWidget = QtWidgets.QWidget(Dialog)
self.layoutWidget.setGeometry(QtCore.QRect(340, 120, 135, 241))
self.layoutWidget.setObjectName("layoutWidget")
self.verticalLayout = QtWidgets.QVBoxLayout(self.layoutWidget)
self.verticalLayout.setContentsMargins(0, 0, 0, 0)
self.verticalLayout.setObjectName("verticalLayout")
self.pushButton_2 = QtWidgets.QPushButton(self.layoutWidget)
self.pushButton_2.setObjectName("pushButton_2")
self.verticalLayout.addWidget(self.pushButton_2)
self.pushButton_3 = QtWidgets.QPushButton(self.layoutWidget)
self.pushButton_3.setObjectName("pushButton_3")
self.verticalLayout.addWidget(self.pushButton_3)
self.pushButton_4 = QtWidgets.QPushButton(self.layoutWidget)
self.pushButton_4.setObjectName("pushButton_4")
self.verticalLayout.addWidget(self.pushButton_4)
self.lineEdit = QtWidgets.QLineEdit(self.layoutWidget)
self.lineEdit.setObjectName("lineEdit")
self.verticalLayout.addWidget(self.lineEdit)
self.pushButton_5 = QtWidgets.QPushButton(self.layoutWidget)
self.pushButton_5.setObjectName("pushButton_5")
self.verticalLayout.addWidget(self.pushButton_5)
self.pushButton = QtWidgets.QPushButton(self.layoutWidget)
self.pushButton.setObjectName("pushButton")
self.verticalLayout.addWidget(self.pushButton)
self.retranslateUi(Dialog)
self.pushButton.clicked.connect(Dialog.btnClose)
self.pushButton_2.clicked.connect(Dialog.btnInsert)
self.pushButton_3.clicked.connect(Dialog.btnDelete)
self.pushButton_4.clicked.connect(Dialog.btnUpdate)
self.pushButton_5.clicked.connect(Dialog.btnQuery)
QtCore.QMetaObject.connectSlotsByName(Dialog)
def retranslateUi(self, Dialog):
_translate = QtCore.QCoreApplication.translate
Dialog.setWindowTitle(_translate("Dialog", "Dialog"))
self.label.setText(_translate("Dialog", "用户管理"))
self.pushButton_2.setText(_translate("Dialog", "增加"))
self.pushButton_3.setText(_translate("Dialog", "删除"))
self.pushButton_4.setText(_translate("Dialog", "修改"))
self.pushButton_5.setText(_translate("Dialog", "查询"))
self.pushButton.setText(_translate("Dialog", "关闭"))
| [
"8584751@qq.com"
] | 8584751@qq.com |
e6b1813c3f8b1b5ec5036f41e3260c00447cd56c | a9e3f3ad54ade49c19973707d2beb49f64490efd | /Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/openedx/core/djangoapps/api_admin/tests/test_views.py | ae07399acb44c8b63d501cadbbf37a3fbe23ed9e | [
"AGPL-3.0-only",
"AGPL-3.0-or-later",
"MIT"
] | permissive | luque/better-ways-of-thinking-about-software | 8c3dda94e119f0f96edbfe5ba60ca6ec3f5f625d | 5809eaca7079a15ee56b0b7fcfea425337046c97 | refs/heads/master | 2021-11-24T15:10:09.785252 | 2021-11-22T12:14:34 | 2021-11-22T12:14:34 | 163,850,454 | 3 | 1 | MIT | 2021-11-22T12:12:31 | 2019-01-02T14:21:30 | JavaScript | UTF-8 | Python | false | false | 15,310 | py | """ Tests for the api_admin app's views. """
import json
import ddt
import httpretty
from django.conf import settings
from django.test import TestCase
from django.test.utils import override_settings
from django.urls import reverse
from oauth2_provider.models import get_application_model
from openedx.core.djangoapps.api_admin.models import ApiAccessConfig, ApiAccessRequest
from openedx.core.djangoapps.api_admin.tests.factories import (
ApiAccessRequestFactory,
ApplicationFactory,
CatalogFactory
)
from openedx.core.djangoapps.api_admin.tests.utils import VALID_DATA
from openedx.core.djangolib.testing.utils import skip_unless_lms
from common.djangoapps.student.tests.factories import UserFactory
Application = get_application_model() # pylint: disable=invalid-name
class ApiAdminTest(TestCase):
"""
Base class to allow API admin access to tests.
"""
def setUp(self):
super().setUp()
ApiAccessConfig(enabled=True).save()
@skip_unless_lms
class ApiRequestViewTest(ApiAdminTest):
"""
Test the API Request View.
"""
def setUp(self):
super().setUp()
self.url = reverse('api_admin:api-request')
password = 'abc123'
self.user = UserFactory(password=password)
self.client.login(username=self.user.username, password=password)
def test_get(self):
"""Verify that a logged-in can see the API request form."""
response = self.client.get(self.url)
assert response.status_code == 200
def test_get_anonymous(self):
"""Verify that users must be logged in to see the page."""
self.client.logout()
response = self.client.get(self.url)
assert response.status_code == 302
def test_get_with_existing_request(self):
"""
Verify that users who have already requested access are redirected
to the client creation page to see their status.
"""
ApiAccessRequestFactory(user=self.user)
response = self.client.get(self.url)
self.assertRedirects(response, reverse('api_admin:api-status'))
def _assert_post_success(self, response):
"""
Assert that a successful POST has been made, that the response
redirects correctly, and that the correct object has been created.
"""
self.assertRedirects(response, reverse('api_admin:api-status'))
api_request = ApiAccessRequest.objects.get(user=self.user)
assert api_request.status == ApiAccessRequest.PENDING
return api_request
def test_post_valid(self):
"""Verify that a logged-in user can create an API request."""
assert not ApiAccessRequest.objects.all().exists()
response = self.client.post(self.url, VALID_DATA)
self._assert_post_success(response)
def test_post_anonymous(self):
"""Verify that users must be logged in to create an access request."""
self.client.logout()
response = self.client.post(self.url, VALID_DATA)
assert response.status_code == 302
assert not ApiAccessRequest.objects.all().exists()
def test_get_with_feature_disabled(self):
"""Verify that the view can be disabled via ApiAccessConfig."""
ApiAccessConfig(enabled=False).save()
response = self.client.get(self.url)
assert response.status_code == 404
def test_post_with_feature_disabled(self):
"""Verify that the view can be disabled via ApiAccessConfig."""
ApiAccessConfig(enabled=False).save()
response = self.client.post(self.url)
assert response.status_code == 404
@skip_unless_lms
@override_settings(PLATFORM_NAME='edX')
@ddt.ddt
class ApiRequestStatusViewTest(ApiAdminTest):
"""
Tests of the API Status endpoint.
"""
def setUp(self):
super().setUp()
password = 'abc123'
self.user = UserFactory(password=password)
self.client.login(username=self.user.username, password=password)
self.url = reverse('api_admin:api-status')
def test_get_without_request(self):
"""
Verify that users who have not yet requested API access are
redirected to the API request form.
"""
response = self.client.get(self.url)
self.assertRedirects(response, reverse('api_admin:api-request'))
@ddt.data(
(ApiAccessRequest.APPROVED, 'Your request to access the edX Course Catalog API has been approved.'),
(ApiAccessRequest.PENDING, 'Your request to access the edX Course Catalog API is being processed.'),
(ApiAccessRequest.DENIED, 'Your request to access the edX Course Catalog API has been denied.'),
)
@ddt.unpack
def test_get_with_request(self, status, expected):
"""
Verify that users who have requested access can see a message
regarding their request status.
"""
ApiAccessRequestFactory(user=self.user, status=status)
response = self.client.get(self.url)
self.assertContains(response, expected)
def test_get_with_existing_application(self):
"""
Verify that if the user has created their client credentials, they
are shown on the status page.
"""
ApiAccessRequestFactory(user=self.user, status=ApiAccessRequest.APPROVED)
application = ApplicationFactory(user=self.user)
response = self.client.get(self.url)
self.assertContains(response, application.client_secret)
self.assertContains(response, application.client_id)
self.assertContains(response, application.redirect_uris)
def test_get_anonymous(self):
"""Verify that users must be logged in to see the page."""
self.client.logout()
response = self.client.get(self.url)
assert response.status_code == 302
def test_get_with_feature_disabled(self):
"""Verify that the view can be disabled via ApiAccessConfig."""
ApiAccessConfig(enabled=False).save()
response = self.client.get(self.url)
assert response.status_code == 404
@ddt.data(
(ApiAccessRequest.APPROVED, True, True),
(ApiAccessRequest.DENIED, True, False),
(ApiAccessRequest.PENDING, True, False),
(ApiAccessRequest.APPROVED, False, True),
(ApiAccessRequest.DENIED, False, False),
(ApiAccessRequest.PENDING, False, False),
)
@ddt.unpack
def test_post(self, status, application_exists, new_application_created):
"""
Verify that posting the form creates an application if the user is
approved, and does not otherwise. Also ensure that if the user
already has an application, it is deleted before a new
application is created.
"""
if application_exists:
old_application = ApplicationFactory(user=self.user)
ApiAccessRequestFactory(user=self.user, status=status)
self.client.post(self.url, {
'name': 'test.com',
'redirect_uris': 'http://example.com'
})
applications = Application.objects.filter(user=self.user)
if application_exists and new_application_created:
assert applications.count() == 1
assert old_application != applications[0]
elif application_exists:
assert applications.count() == 1
assert old_application == applications[0]
elif new_application_created:
assert applications.count() == 1
else:
assert applications.count() == 0
def test_post_with_errors(self):
ApiAccessRequestFactory(user=self.user, status=ApiAccessRequest.APPROVED)
response = self.client.post(self.url, {
'name': 'test.com',
'redirect_uris': 'not a url'
})
self.assertContains(response, 'Enter a valid URL.')
@skip_unless_lms
class ApiTosViewTest(ApiAdminTest):
"""
Tests of the API terms of service endpoint.
"""
def test_get_api_tos(self):
"""
Verify that the terms of service can be read.
"""
url = reverse('api_admin:api-tos')
response = self.client.get(url)
self.assertContains(response, 'Terms of Service')
class CatalogTest(ApiAdminTest):
"""
Test the catalog API.
"""
def setUp(self):
super().setUp()
password = 'abc123'
self.user = UserFactory(password=password, is_staff=True)
self.client.login(username=self.user.username, password=password)
def mock_catalog_endpoint(self, data, catalog_id=None, method=httpretty.GET, status_code=200):
""" Mock the Course Catalog API's catalog endpoint. """
assert httpretty.is_enabled(), 'httpretty must be enabled to mock Catalog API calls.'
url = '{root}/catalogs/'.format(root=settings.COURSE_CATALOG_API_URL.rstrip('/'))
if catalog_id:
url += f'{catalog_id}/'
httpretty.register_uri(
method,
url,
body=json.dumps(data),
content_type='application/json',
status=status_code
)
@skip_unless_lms
class CatalogSearchViewTest(CatalogTest):
"""
Test the catalog search endpoint.
"""
def setUp(self):
super().setUp()
self.url = reverse('api_admin:catalog-search')
def test_get(self):
response = self.client.get(self.url)
assert response.status_code == 200
@httpretty.activate
def test_post(self):
catalog_user = UserFactory()
self.mock_catalog_endpoint({'results': []})
response = self.client.post(self.url, {'username': catalog_user.username})
self.assertRedirects(response, reverse('api_admin:catalog-list', kwargs={'username': catalog_user.username}))
def test_post_without_username(self):
response = self.client.post(self.url, {'username': ''})
self.assertRedirects(response, reverse('api_admin:catalog-search'))
@skip_unless_lms
class CatalogListViewTest(CatalogTest):
"""
Test the catalog list endpoint.
"""
def setUp(self):
super().setUp()
self.catalog_user = UserFactory()
self.url = reverse('api_admin:catalog-list', kwargs={'username': self.catalog_user.username})
@httpretty.activate
def test_get(self):
catalog = CatalogFactory(viewers=[self.catalog_user.username])
self.mock_catalog_endpoint({'results': [catalog.attributes]})
response = self.client.get(self.url)
self.assertContains(response, catalog.name)
@httpretty.activate
def test_get_no_catalogs(self):
"""Verify that the view works when no catalogs are set up."""
self.mock_catalog_endpoint({}, status_code=404)
response = self.client.get(self.url)
assert response.status_code == 200
@httpretty.activate
def test_post(self):
catalog_data = {
'name': 'test-catalog',
'query': '*',
'viewers': [self.catalog_user.username]
}
catalog_id = 123
self.mock_catalog_endpoint(dict(catalog_data, id=catalog_id), method=httpretty.POST)
response = self.client.post(self.url, catalog_data)
assert httpretty.last_request().method == 'POST'
self.mock_catalog_endpoint(CatalogFactory().attributes, catalog_id=catalog_id)
self.assertRedirects(response, reverse('api_admin:catalog-edit', kwargs={'catalog_id': catalog_id}))
@httpretty.activate
def test_post_invalid(self):
catalog = CatalogFactory(viewers=[self.catalog_user.username])
self.mock_catalog_endpoint({'results': [catalog.attributes]})
response = self.client.post(self.url, {
'name': '',
'query': '*',
'viewers': [self.catalog_user.username]
})
assert response.status_code == 400
# Assert that no POST was made to the catalog API
assert len([r for r in httpretty.httpretty.latest_requests if r.method == 'POST']) == 0
@skip_unless_lms
class CatalogEditViewTest(CatalogTest):
"""
Test edits to the catalog endpoint.
"""
def setUp(self):
super().setUp()
self.catalog_user = UserFactory()
self.catalog = CatalogFactory(viewers=[self.catalog_user.username])
self.url = reverse('api_admin:catalog-edit', kwargs={'catalog_id': self.catalog.id})
@httpretty.activate
def test_get(self):
self.mock_catalog_endpoint(self.catalog.attributes, catalog_id=self.catalog.id)
response = self.client.get(self.url)
self.assertContains(response, self.catalog.name)
@httpretty.activate
def test_delete(self):
self.mock_catalog_endpoint(
self.catalog.attributes,
method=httpretty.DELETE,
catalog_id=self.catalog.id
)
response = self.client.post(self.url, {'delete-catalog': 'on'})
self.assertRedirects(response, reverse('api_admin:catalog-search'))
assert httpretty.last_request().method == 'DELETE' # lint-amnesty, pylint: disable=no-member
assert httpretty.last_request().path == \
f'/api/v1/catalogs/{self.catalog.id}/' # lint-amnesty, pylint: disable=no-member
assert len(httpretty.httpretty.latest_requests) == 1
@httpretty.activate
def test_edit(self):
self.mock_catalog_endpoint(self.catalog.attributes, method=httpretty.PATCH, catalog_id=self.catalog.id)
new_attributes = dict(self.catalog.attributes, **{'delete-catalog': 'off', 'name': 'changed'})
response = self.client.post(self.url, new_attributes)
self.mock_catalog_endpoint(new_attributes, catalog_id=self.catalog.id)
self.assertRedirects(response, reverse('api_admin:catalog-edit', kwargs={'catalog_id': self.catalog.id}))
@httpretty.activate
def test_edit_invalid(self):
self.mock_catalog_endpoint(self.catalog.attributes, catalog_id=self.catalog.id)
new_attributes = dict(self.catalog.attributes, **{'delete-catalog': 'off', 'name': ''})
response = self.client.post(self.url, new_attributes)
assert response.status_code == 400
# Assert that no PATCH was made to the Catalog API
assert len([r for r in httpretty.httpretty.latest_requests if r.method == 'PATCH']) == 0
@skip_unless_lms
class CatalogPreviewViewTest(CatalogTest):
"""
Test the catalog preview endpoint.
"""
def setUp(self):
super().setUp()
self.url = reverse('api_admin:catalog-preview')
@httpretty.activate
def test_get(self):
data = {'count': 1, 'results': ['test data'], 'next': None, 'prev': None}
httpretty.register_uri(
httpretty.GET,
'{root}/courses/'.format(root=settings.COURSE_CATALOG_API_URL.rstrip('/')),
body=json.dumps(data),
content_type='application/json'
)
response = self.client.get(self.url, {'q': '*'})
assert response.status_code == 200
assert json.loads(response.content.decode('utf-8')) == data
def test_get_without_query(self):
response = self.client.get(self.url)
assert response.status_code == 200
assert json.loads(response.content.decode('utf-8')) == {'count': 0, 'results': [], 'next': None, 'prev': None}
| [
"rafael.luque@osoco.es"
] | rafael.luque@osoco.es |
caea401db9732eca5720a2dcdcfb26d14c37b340 | 3fed2e41269a397ac5fdc641f54e6dbfd0f00e2b | /dice/dice.py | 421fac1a42204e9fe864b3091654c0174cc3c989 | [] | no_license | 2013edp11badam/infotech | f1bd80a08f30a9fa59598175648ccf675eac47b3 | 0b8fe295615639fc5ef485fd07712b64f2ef5a37 | refs/heads/master | 2016-09-05T15:27:54.944508 | 2013-06-13T15:39:52 | 2013-06-13T15:39:52 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 509 | py | #!/usr/bin/python
from fltk import *
import random, glob
def roll(w_id):
random.shuffle(images)
image1 = Fl_JPEG_Image(images[0])
box1.image(image1.copy(box1.w(), box1.h()))
box1.redraw()
images = []
for image in glob.glob("images/dice*.jpg"):
images.append(image)
window = Fl_Window(100, 100, 260, 290, 'Roll Dice')
window.begin()
box1 = Fl_Box(5, 5, 250, 250)
button1 = Fl_Return_Button(5, 260, 250, 25, 'Roll Dice')
roll(window)
window.end()
button1.callback(roll)
window.show()
Fl.run()
| [
"2013edp11badam@gmail.com"
] | 2013edp11badam@gmail.com |
70b11fb79888d098da78da5b1165b55e6625abf6 | bd675e3137f6e37b33bdf3a95aa7bc6d1b5545bb | /WebProject/WebProject/webshop/tests/tests_of_functions.py | add17f783014e43136b7959b342e5d8d28124209 | [] | no_license | LiaKolt/Webshop | 3d7b3cac7daca91dfca8b70e005fee2316fe348e | 143f431b7c89d66b66d7d46cd6e518d2c351d1fe | refs/heads/master | 2022-10-06T16:33:51.214139 | 2020-01-04T10:05:02 | 2020-01-04T10:05:02 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,310 | py | from django.conf import settings
from django.http import HttpRequest
from django.test import TestCase
#узнать что за библиотека
from importlib import import_module
import webshop.functions
from webshop.models import Cart
class GetOrCreateCartTest(TestCase):
@classmethod
def setUpTestData(cls):
cls.session_key = None
cls.engine = import_module(settings.SESSION_ENGINE)
cls.request = HttpRequest()
cls.request.session = cls.engine.SessionStore(cls.session_key)
def test_create_object_cart(self):
webshop.functions.create_cart(self.request)
self.assertEqual(Cart.objects.count(), 1)
def test_return_created_cart(self):
cart = webshop.functions.create_cart(self.request)
cart_id = self.request.session['cart_id']
self.assertEqual(cart, Cart.objects.get(id=cart_id))
def test_get_cart(self):
self.request.session = self.engine.SessionStore(self.session_key)
self.request.session['cart_id'] = 1
Cart.objects.create(id=1)
self.cart = webshop.functions.get_cart(self.request)
self.assertEqual(self.cart, Cart.objects.get(id=1))
def test_get_or_create_cart(self):
cart, created = webshop.functions.get_or_create_cart(self.request)
self.assertTrue(created)
cart, created = webshop.functions.get_or_create_cart(self.request)
self.assertFalse(created) | [
"enotukit@gmail.com"
] | enotukit@gmail.com |
cc6e7193171f08b4bec062c3ccbc1f92ae8d7cf2 | 1eefbdafb2865b54ef6a8eb956d3ab9d5497f3c5 | /inria/Code/install_packages.py | 572b28f163f8be970a3aa8e54bda27bc690131a4 | [] | no_license | BRGM/hubeau | af5048c698f813c35addaa34502887e2f2743ee5 | 55fa39e74c34150b458ae034b17eec039bb4edf5 | refs/heads/master | 2023-08-05T00:59:24.869324 | 2023-08-03T08:46:09 | 2023-08-03T08:46:09 | 133,785,135 | 58 | 8 | null | 2023-08-03T08:46:11 | 2018-05-17T08:45:13 | Jupyter Notebook | UTF-8 | Python | false | false | 286 | py | import sys
import subprocess
# implement pip as a subprocess:
requirements = ["requests", "numpy", "nltk", "flair", "ip2geotools", "ipinfo", "datetime", "stanza", "pandas", "termcolor"]
for pkg in requirements:
subprocess.check_call([sys.executable, '-m', 'pip', 'install', pkg])
| [
"mayatouzari@gmail.com"
] | mayatouzari@gmail.com |
16180e8ffe25cf0b0e5dac347e8b9032e1205bf6 | 337b133f562836be90841dc0353ce820bcdea2a6 | /opcode.py | b2bdd31369867202d30e5eb01b27a6c9f6cfe8c6 | [] | no_license | noriok/pdp11 | 9683fd44f99c01f28095e4dabf45328c8da88f99 | e33d7477eaab6d41afe474d6ccc25607164cbf2f | refs/heads/master | 2020-05-19T15:09:45.364363 | 2012-12-22T14:05:00 | 2012-12-22T14:11:58 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,585 | py | # coding: utf-8
import util
import opdump
import oprun
import run
def dispatch(op, data, mode, vm):
def op_match(a, b):
assert len(a) == len(b), (len(a), len(b))
for i in range(len(a)):
if a[i].isdigit() and b[i].isdigit():
if a[i] != b[i]:
return False
return True
if 0o104400 <= op <= 0o104777: # Trap
# print("TRAP:", op, oct(op))
return ["sys", (op & 0xff)]
# TODO: データを読むところを統一する
if vm is not None:
data.set_position(vm.pc() + 2) # +2 はop自身の分
op8 = "%06o" % op # 8進に変換
for (opcode, dumpfn, dofn) in optable:
opcode = opcode.replace(" ", "")
if op_match(op8, opcode):
if mode == run.MODE_DUMP:
return dumpfn(op, data)
elif mode == run.MODE_EXEC:
return dofn(vm, op, data)
else:
assert False
else:
# print("not matched:", op, oct(op), hex(op))
if mode == run.MODE_DUMP:
return [".word", oct(op)]
else:
assert False, ("unimplement:%d" % opcode)
d = opdump
r = oprun
optable = [
("00 00 06", d.rtt, r.rtt),
("00 01 DD", d.jmp, r.jmp),
("00 02 0R", d.rts, r.rts),
("00 4R DD", d.jsr, r.jsr),
("00 50 DD", d.clr, r.clr),
("00 57 DD", d.tst, r.tst),
("01 SS DD", d.mov, r.mov),
("02 SS DD", d.cmp, r.cmp),
("10 30 XX", d.bcc, r.bcc),
("17 00 11", d.setd, r.setd),
]
if __name__ == "__main__":
pass
| [
"noriokob@gmail.com"
] | noriokob@gmail.com |
551c6d0779faa323b290061e22848c4b22a59462 | 986eac22aaac1762a2ccc1c6faa09a283cad1245 | /imdb/tests.py | 47befb9b72a80f2fbb227bab5ead6607fd549458 | [] | no_license | Fuzail96/fynd | 582d2311cd182573049b98e2d3048ea66269cedb | aebaa39badad16fb51c52e8c0eba340f0afb237a | refs/heads/main | 2023-04-26T06:55:35.299658 | 2021-05-09T10:13:22 | 2021-05-09T10:13:22 | 365,524,693 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,365 | py | from django.test import RequestFactory,TestCase, Client
from django.urls import reverse
from .models import Movie, Genre
from ..user.models import User
from rest_framework.permissions import IsAdminUser
from .views import create_movies, update_movies, delete_movies
class SearchAndGetMovies(TestCase):
def setUp(self):
self.client = Client()
self.genre = Genre.objects.create(name='Drama')
self.genre.save()
self.movie = Movie.objects.create(name='movie1', director='test_director', popularity=89.0, imdb_score=8.9)
self.movie.save()
self.movie.genre.add(*[self.genre])
def test_search(self):
# test with no query string
url = reverse('search_movie', args=(None,))
response = self.client.get(url)
self.assertEqual(response.status, 400)
resp_data = {'msg': 'invalid input for lookup.'}
self.assertEqual(resp_data, response.json())
# test with query string
url = reverse('search_movie', args=('mo',))
response = self.client.get(url)
self.assertEqual(response.status, 200)
resp_data = {"data": [{"id": 1, "genre": [{"id": 1, "name": "Drama"}], "name": "movie1", "director":
"test_director", "popularity": 89, "imdb_score": 8.9}]}
self.assertEqual(resp_data, response.json())
def test_get_movie(self):
# test with id which doesn't exists.
url = reverse('get_movie', args=(2,))
response = self.client.get(url)
self.assertEqual(response.status, 400)
resp_data = {'msg': 'Movie does not exist.'}
self.assertEqual(resp_data, response.json())
# test with id which exists.
url = reverse('get_movie', args=(1,))
response = self.client.get(url)
self.assertEqual(response.status, 200)
resp_data = {'data': {"id": 1, "genre": [{"id": 1, "name": "Drama"}], "name": "movie1", "director":
"test_director", "popularity": 89, "imdb_score": 8.9}}
self.assertEqual(resp_data, response.json())
class AllMovieTest(TestCase):
def setUp(self):
self.client = Client()
self.admin = User.objects.create_user(email='admin2@example.com', password='admintest12', name='test_admin',
phone='1234567890', is_staff=True)
self.admin.save()
self.user = User.objects.create_user(email='user2@example.com', password='usertest12', name='test_user',
phone='0987654321')
self.user.save()
self.factory = RequestFactory()
def create_movie(self):
# test with admin access.
url = reverse('create_movie')
req_data = {
"genre": [
{
"name": "Crime"
}
],
"name": "Movie2",
"director": "test_director2",
"popularity": 67.0,
"imdb_score": 6.7
}
request = self.factory.post(url, req_data)
request.user = self.admin
permission_check = IsAdminUser()
permission = permission_check.has_permission(request, None)
self.assertTrue(permission)
response = create_movies(request)
self.assertEqual(response.status, 201)
resp_data = {'msg': 'Movie created.'}
self.assertEqual(resp_data, response.json())
# test with user access.
request.user = self.user
permission_check = IsAdminUser()
permission = permission_check.has_permission(request, None)
self.assertTrue(permission)
response = create_movies(request)
self.assertEqual(response.status, 403)
resp_data = {"detail": "You do not have permission to perform this action."}
self.assertEqual(resp_data, response.json())
def update_movie(self):
# test with admin access.
url = reverse('update_movie', args=(1,))
req_data = {
"genre": [
{
"name": "Drama"
}
],
"name": "Movie21",
"director": "test_director21",
"popularity": 63.0,
"imdb_score": 6.3
}
request = self.factory.post(url, req_data)
request.user = self.admin
permission_check = IsAdminUser()
permission = permission_check.has_permission(request, None)
self.assertTrue(permission)
response = update_movies(request)
self.assertEqual(response.status, 200)
resp_data = {'msg': 'Movie updated.'}
self.assertEqual(resp_data, response.json())
# test with id which doesn't exist.
url = reverse('update_movie', args=(8,))
req_data = {
"genre": [
{
"name": "Drama"
}
],
"name": "Movie21",
"director": "test_director21",
"popularity": 63.0,
"imdb_score": 6.3
}
request = self.factory.post(url, req_data)
request.user = self.admin
permission_check = IsAdminUser()
permission = permission_check.has_permission(request, None)
self.assertTrue(permission)
response = update_movies(request)
self.assertEqual(response.status, 400)
resp_data = {'msg': 'Movie does not exist.'}
self.assertEqual(resp_data, response.json())
# test with user access.
request.user = self.user
permission_check = IsAdminUser()
permission = permission_check.has_permission(request, None)
self.assertTrue(permission)
response = update_movies(request)
self.assertEqual(response.status, 403)
resp_data = {"detail": "You do not have permission to perform this action."}
self.assertEqual(resp_data, response.json())
def delete_movie(self):
# test with admin access.
url = reverse('delete_movie', args=(1,))
request = self.factory.post(url)
request.user = self.admin
permission_check = IsAdminUser()
permission = permission_check.has_permission(request, None)
self.assertTrue(permission)
response = delete_movies(request)
self.assertEqual(response.status, 200)
resp_data = {'msg': 'Movie deleted.'}
self.assertEqual(resp_data, response.json())
# test with id which doesn't exist.
url = reverse('update_movie', args=(8,))
request = self.factory.post(url)
request.user = self.admin
permission_check = IsAdminUser()
permission = permission_check.has_permission(request, None)
self.assertTrue(permission)
response = delete_movies(request)
self.assertEqual(response.status, 400)
resp_data = {'msg': 'Movie does not exist.'}
self.assertEqual(resp_data, response.json())
# test with user access.
request.user = self.user
permission_check = IsAdminUser()
permission = permission_check.has_permission(request, None)
self.assertTrue(permission)
response = delete_movies(request)
self.assertEqual(response.status, 403)
resp_data = {"detail": "You do not have permission to perform this action."}
self.assertEqual(resp_data, response.json()) | [
"fuzail@codewave.com"
] | fuzail@codewave.com |
505ce4ae23367ad7ce4f6520d7f73f81355fdb4c | 51b3620926ed448974ccd902e02264742f35867d | /projects/ide/sublime/src/Bolt/ui/chain/chain.py | b53f2fe6dd64d533e135edd80654713b77969f82 | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | boltjs/bolt | 56295e30876eaea8be2f4a276e0201f85b1bfd99 | c2666c876b34b1a61486a432eef3141ca8d1e411 | refs/heads/master | 2016-09-08T02:34:47.202062 | 2013-09-03T06:55:53 | 2013-09-03T06:55:53 | 7,078,233 | 11 | 0 | null | 2013-07-14T12:01:26 | 2012-12-09T11:55:40 | JavaScript | UTF-8 | Python | false | false | 168 | py |
def go(window, pipeline):
def next_pipe(num, values):
pipeline[num].next(window, values, num, next_pipe)
pipeline[0].next(window, [], 0, next_pipe)
| [
"dev@vapid.(none)"
] | dev@vapid.(none) |
9e5e0e10855a710a5aad2148695dbd378648c068 | 98b203d8ecf2f51ab0f7707eeee09ee07d109577 | /python/khans_topological.py | a4aa125035846d67c37d12b1613209759391af22 | [] | no_license | razerboot/DataStrcutures-and-Algorithms | 9967727a730fa59daa00a91c021042d885584b10 | b47560efe4fa59ae255dc83c791e18acd9813c22 | refs/heads/master | 2021-07-16T19:08:16.770941 | 2021-01-21T11:19:19 | 2021-01-21T11:19:19 | 95,435,549 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 760 | py | from collections import defaultdict
# using khan's algo
def cyclecheck(graph, n):
degree = [0] * (n + 1)
for u in graph:
for v in graph[u]:
degree[v] += 1
q = []
for u in xrange(1, n + 1):
if degree[u] == 0:
q.append(u)
count = 0
while (q != []):
u = q.pop()
for v in graph[u]:
degree[v] -= 1
if degree[v] == 0:
q.append(v)
count += 1
if count != n:
return 1
else:
return 0
t = input()
for a0 in xrange(t):
n, m = map(int, raw_input().split())
graph = defaultdict(set)
for a1 in xrange(m):
u, v = map(int, raw_input().split())
graph[u].add(v)
print cyclecheck(graph, n) | [
"akshaykumar@akshaymac.local"
] | akshaykumar@akshaymac.local |
fc216e0eba3e9a86e7881f4b25e16d38a4763a8f | 1b4707f03163ce2d3a787155d442194d3de9dd9e | /infomapcog/utils.py | 8b09d3323305ac9b0a11d476a652168ffb5c8c98 | [] | no_license | PhyloStar/CogDetect | 37d0537a16698ef3df59cb1c39551516009e75de | 48e8e7a412e744f1fe2de6c4abd145ad42d8889c | refs/heads/master | 2021-01-20T17:02:32.697694 | 2017-05-25T17:24:07 | 2017-05-25T17:24:07 | 82,854,261 | 2 | 1 | null | 2017-05-11T13:27:41 | 2017-02-22T21:27:26 | Python | UTF-8 | Python | false | false | 1,665 | py | from collections import defaultdict
import itertools
import numpy as np
def cleanASJP(w):
w = w.replace("%","")
w = w.replace("K","k")
w = w.replace("~","")
w = w.replace("*","")
w = w.replace("\"","")
w = w.replace("$","")
return w
def writeNexus(dm,f):
l=len(dm)
f.write("#nexus\n"+
"\n")
f.write("BEGIN Taxa;\n")
f.write("DIMENSIONS ntax="+str(l)+";\n"
"TAXLABELS\n"+
"\n")
i=0
for ka in dm:
f.write("["+str(i+1)+"] '"+ka+"'\n")
i=i+1
f.write(";\n"+
"\n"+
"END; [Taxa]\n"+
"\n")
f.write("BEGIN Distances;\n"
"DIMENSIONS ntax="+str(l)+";\n"+
"FORMAT labels=left;\n")
f.write("MATRIX\n")
i=0
for ka in dm:
row="["+str(i+1)+"]\t'"+ka+"'\t"
for kb in dm:
row=row+str(dm[ka][kb])+"\t"
f.write(row+"\n")
f.write(";\n"+
"END; [Distances]\n"+
"\n"+
"BEGIN st_Assumptions;\n"+
" disttransform=NJ;\n"+
" splitstransform=EqualAngle;\n"+
" SplitsPostProcess filter=dimension value=4;\n"+
" autolayoutnodelabels;\n"+
"END; [st_Assumptions]\n")
f.flush()
def dict2binarynexus(d, ex_langs, langs):
binArr = []
x = defaultdict(lambda: defaultdict(int))
for lang, clusterID in d.items():
x[clusterID][lang] = 1
for clusterID in x.keys():
temp = []
for lang in langs:
if lang in ex_langs: temp.append(2)
else:
temp += [x[clusterID][lang]]
binArr.append(temp)
return binArr
| [
"taraka@fripost.org"
] | taraka@fripost.org |
91653d32a3b1b10a52e1b7c54370a3afb71dc22f | d1a798273bae6a208adbeb68036802270d3087f8 | /crowddrone_input_aggregator/scripts/input_aggregator_server.py | 6954e6733043f61f4fc229d42e529cd8eb7a8624 | [] | no_license | ElliotSalisbury/CrowdDrone | ba7642e2dac6f75a59629aed13dff4019971fe8a | 33a526bf0e85b9d2c5d3fed927754e1ab36d4f1e | refs/heads/master | 2020-05-18T09:31:30.626705 | 2017-06-20T08:43:55 | 2017-06-20T08:43:55 | 31,028,191 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 6,499 | py | #!/usr/bin/env python
import sys
import importlib
from threading import Thread, Lock
from threading2 import SHLock
import rospy
from geometry_msgs.msg import Twist
from std_msgs.msg import Bool, String
from input_aggregator.aggregator import Aggregator
from crowddrone_input_aggregator.srv import *
import random
robotName = "ardrone"
connectedClients = {}
connectedLock = SHLock()
clientListeners = []
aggregatorInstance = None
def addClient(client):
connectedClients[client.topic] = client
#incase clients are publishing to topics before this server runs
class TopicFinder(Thread):
def __init__(self,namespace="/"):
Thread.__init__(self)
self.namespace = str(namespace)
def run(self):
#once a second, check if topics have been published without coming through the new client service
rate = rospy.Rate(1)
while not rospy.is_shutdown():
topics = rospy.get_published_topics(self.namespace)
for topic in topics:
if topic[0].endswith("HB"):
continue #we dont want to connect to the heartbeat topics
if not topic[0] in connectedClients:
connectedLock.acquire(shared=False)
addClient(Client(topic[0], "unknown", "unknown", "unknown", "unknown"))
connectedLock.release()
rate.sleep()
#handle a request to become a new client
def handle_new_client(req):
connectedLock.acquire(shared=False)
clientCount = len(connectedClients) + 1
topic = "/input_aggregator/cmd_vel_%s"%clientCount
while topic in connectedClients:
clientCount = clientCount + 1
topic = "/input_aggregator/cmd_vel_%s"%clientCount
addClient(Client(topic, req.assignmentId, req.hitId, req.workerId, req.turkSubmitTo))
connectedLock.release()
return GetNewClientTopicResponse(topic)
def handle_connected_info(req):
connectedLock.acquire(shared=True)
count = 0
hitIds = []
workerIds = []
assignmentIds = []
for topic in connectedClients:
client = connectedClients[topic]
if client.isAlive:
count += 1
hitIds.append(client.hitId)
workerIds.append(client.workerId)
assignmentIds.append(client.assignmentId)
connectedLock.release()
return GetConnectedInfoResponse(count, hitIds, workerIds, assignmentIds)
class Client:
def __init__(self,topic, assignmentId, hitId, workerId, turkSubmitTo):
self.topic = topic
self.assignmentId = assignmentId
self.hitId = hitId
self.workerId = workerId
self.turkSubmitTo = turkSubmitTo
self.isAlive = True
self.lastHeartbeat = rospy.get_rostime()
self.msgs = []
self.msgLock = Lock()
self.HOLDFOR = 5
self.DURATION = 2
self.fakeping = random.random() * 0
rospy.loginfo("client connected: %s - %s %s %s %s", self.topic, self.assignmentId, self.hitId, self.workerId, self.turkSubmitTo)
rospy.Subscriber(self.topic, Twist, self.msgReceived)
rospy.Subscriber(self.topic+"/HB", Bool, self.heartbeatReceived)
Thread(target=self.timeout).start()
def msgReceived(self, msg):
self.heartbeatReceived(msg)
storedmsg = [rospy.get_rostime(), msg]
with self.msgLock:
self.msgs.append(storedmsg)
self.clearOldMsgs(storedmsg[0])
rospy.loginfo("client msgs: %s - %s", self.topic, storedmsg)
Thread(target=self.notifyListeners).start()
def clearOldMsgs(self, now):
try:
#clear anything older than 5 seconds
old = now - rospy.Duration(self.HOLDFOR)
self.msgs = [msg for msg in self.msgs if msg[0] > old]
except TypeError: #catch negative times
pass
def msgsFromEpoch(self, now):
try:
old = now - rospy.Duration(self.DURATION)
except TypeError: #catch negative times
return []
rmsgs = []
if self.msgs:
with self.msgLock:
for index in reversed(xrange(len(self.msgs))):
msg = self.msgs[index]
if msg[0] <= now:
if msg[0] > old:
rmsgs.insert(0,msg)
else:
break
return rmsgs
def heartbeatReceived(self, msg):
self.isAlive = True
self.lastHeartbeat = rospy.get_rostime()
def timeout(self):
while not rospy.is_shutdown():
if(self.isAlive and self.lastHeartbeat + rospy.Duration(10) < rospy.get_rostime()):
self.isAlive = False
rospy.loginfo("client timeout: %s",self.topic)
self.notifyListeners()
rospy.sleep(10.0)
def notifyListeners(self):
if self.fakeping > 0:
rospy.sleep(self.fakeping)
for listener in clientListeners:
listener.notifyClientUpdated(self.topic)
def switchAggregator(moduleName):
global aggregatorInstance
if hasattr(moduleName, 'data'):
moduleName=moduleName.data
aggregatorClass = getModuleClass(moduleName)
if aggregatorClass:
if aggregatorInstance and aggregatorInstance.__class__ == aggregatorClass:
return None #bail if the aggregator is the same
if aggregatorInstance:
clientListeners.remove(aggregatorInstance)
aggregatorInstance.stop()
aggregatorInstance = aggregatorClass(connectedClients, connectedLock, 'ai_decorator/'+robotName+'/cmd_vel')
aggregatorInstance.start()
clientListeners.append(aggregatorInstance)
rospy.loginfo("aggregator swap: %s", moduleName)
rospy.logwarn("aggregator swap: %s", moduleName)
def getModuleClass(moduleName):
if not moduleName.endswith("_aggregator"):
moduleName = moduleName + "_aggregator"
moduleName = "input_aggregator." + moduleName
module = None
try:
module = importlib.import_module(moduleName)
except ImportError:
rospy.logwarn("No module named: %s", moduleName)
return None
for name in dir(module):
#we dont want to get the abstract baseclass
if(name == "Aggregator"):
continue
#check if this name is a subclass of the Aggregator baseclass
obj = getattr(module, name)
try:
if issubclass(obj, Aggregator):
return obj
except TypeError: # If 'obj' is not a class
pass
return None
if __name__ == "__main__":
#make sure we have the arguments for loading the aggregator class module
argv = rospy.myargv(sys.argv)
if not len(argv) == 3:
print("Incorrect Usage: You must pass in the name of the aggregator module and the name of the robot to control\n%s [AggregatorModule] [RobotName]"%sys.argv[0])
sys.exit(1)
robotName = argv[2]
#initialize the ros node, the topic finder and the new client topic service
rospy.init_node("input_aggregator_server")
TopicFinder("input_aggregator").start()
rospy.Service('get_new_client_topic', GetNewClientTopic, handle_new_client)
rospy.Service('get_connected_info', GetConnectedInfo, handle_connected_info)
#initialize the aggregator class and start its thread
switchAggregator(argv[1])
rospy.Subscriber("change_input_aggregator", String, switchAggregator)
rospy.spin()
| [
"elliot.sal@gmail.com"
] | elliot.sal@gmail.com |
9ba5478576b15fb2df4844d41eafa996dd40b554 | 0dec0ea272260374f680c81f3b4b0827feb69694 | /examples/chinese_test.py | ef7bf8e222dd551a2b28ea0708c37ed6a5e7b54f | [
"MIT"
] | permissive | mapix/redis-completion | 7f4fc00fae8654e2488f1459d64a02fd188f6ebe | 7c72eb70622aaea9e7a226eb2661e1de4e8060f7 | refs/heads/master | 2021-01-16T17:51:40.951676 | 2013-09-11T06:43:15 | 2013-09-11T06:43:15 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,188 | py | # -*- coding:utf-8 -*-
import mmseg
from pinyin import Pinyin
from redis_completion import RedisEngine
pinyin = Pinyin()
engine = RedisEngine()
def store_movie(movie):
phrase = movie["title"]
seg_phrase = " ".join(mmseg.seg_txt(phrase))
_pinyin_phrase = pinyin.get_pinyin(phrase)
py_phrase = "".join([p[0] for p in _pinyin_phrase]).encode("utf-8")
pinyin_phrase = "".join(_pinyin_phrase).encode("utf-8")
phrase = "%s %s %s %s" % (phrase, seg_phrase, pinyin_phrase, py_phrase)
engine.store_json(movie["id"], phrase, movie)
def load_datas():
movies = [
{"id":20513051, "title": "被偷走的那五年", "director":"黄真真"}
, {"id":20513052, "title": "十面埋伏", "director":"张艺谋"}
, {"id":20513053, "title": "龙门镖局", "director":"王勇"}
, {"id":20513054, "title": "致我们终将逝去的青春", "director":"赵薇"}
, {"id":20513055, "title": "金枝欲孽2", "director":"戚其义"}
]
for movie in movies:
store_movie(movie)
load_datas()
for phrase in ["偷", "lmb", "jinzhi"]:
print "Search for:", phrase
for i in engine.search_json(phrase):
print i["title"], i
| [
"luoweifeng@douban.com"
] | luoweifeng@douban.com |
808bcbe7a388b4c525f883d431c665fa2053eeac | d5bfa8beb034b7af6ab4c08a5fb68b19c870bced | /company_tool/shared/thing.py | 373032b3473d381c5c3cd2c171636f01760b0072 | [
"BSD-3-Clause"
] | permissive | ops-guru/project-skeleton-python | 7cd2fc7f57caa6060c289f323e412d9088210259 | 91bceba198f79f1dcb02b6745d0229090d59821c | refs/heads/master | 2020-04-21T06:33:48.170043 | 2019-02-06T08:24:46 | 2019-02-06T08:24:46 | 169,368,688 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 65 | py |
def shared_function(x, sep=':'):
return sep.join(['got', x]) | [
"max@opsguru.io"
] | max@opsguru.io |
f8861f7334eb20d8f066a25e107cb711b639faa4 | c0c820be99a4539dcb9904ebd5ffae0a8953b33f | /Parallel_ACO_Solver/data/pants/world.py | df0fd421d74c64afeed30dd5926e736207cc1357 | [
"MIT"
] | permissive | MrBadge/Parallel_Ant_Colony | d468904eead53dc959855e112c500e89e7705cc1 | 8abbb8113b8ad0f836c8181fc5f79e5dbba8eb95 | refs/heads/master | 2022-12-12T20:45:23.132411 | 2017-07-03T11:22:57 | 2017-07-03T11:22:57 | 96,044,439 | 7 | 2 | MIT | 2022-12-07T23:58:23 | 2017-07-02T19:26:44 | Java | UTF-8 | Python | false | false | 27 | py | Docker-slave/pants/world.py | [
"malcevanatoly@gmail.com"
] | malcevanatoly@gmail.com |
c462bafef5399e8f9cd37b8a37573720063ab2c2 | 306d2a92fb331aec6ddf0794b538d6e3385a0df9 | /app/api/account/urls.py | 21f884031d1962d2ca3574afe6cc2097735a669d | [] | no_license | Zarinabonu/ForceApp | f343d3a52aee08890230c5425c9e238df99c5a7f | 13f8e8613999c4850fc6f0bfcec66f897eecbe4a | refs/heads/master | 2020-12-10T08:00:25.072289 | 2020-01-20T13:14:07 | 2020-01-20T13:14:07 | 233,540,795 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 279 | py | from rest_framework.serializers import ModelSerializer
from app.model import Account
class AccountSerializer(ModelSerializer):
class Meta:
model = Account
fields = ('id',
'f_name',
'l_name',
'm_name',)
| [
"zarinabonu199924@gmail.com"
] | zarinabonu199924@gmail.com |
b43dc33f7a123f7f218d3e8b63e01cafd7ab5c7d | 56b7017a7cfd0329e73cab8fac36db1d6e5797d8 | /15.py | 24e23da030526ab032b0b7c21a17c6142ea4ca3a | [] | no_license | amalphonse/Python_WorkBook | 1d0df468c858960be89ec507f0cc82ffb0154e11 | ae2d6cea2421ef15af3fb67fc6fed05198764b7d | refs/heads/master | 2021-01-17T07:30:29.660469 | 2017-03-02T22:42:47 | 2017-03-02T22:42:47 | 83,731,980 | 2 | 4 | null | null | null | null | UTF-8 | Python | false | false | 107 | py | # -*- coding: utf-8 -*-
"""
Created on Wed Feb 8 21:23:06 2017
@author: Anju
"""
my_dict = {"a":1,"b":2} | [
"anjumercian85@gmail.com"
] | anjumercian85@gmail.com |
08062448f3c156d978a7e8433af6ca9c1541e4b5 | 1e4d2a66f92b8ef3baddaf76366c1be4ad853328 | /JALADI_DSC510/HELLO_WORLD.py | bf983639cf923a54dacf4fe2adbce2e24b684082 | [] | no_license | dlingerfelt/DSC-510-Fall2019 | 0c4168cf030af48619cfd5e044f425f1f9d376dd | 328a5a0c8876f4bafb975345b569567653fb3694 | refs/heads/master | 2022-12-04T05:04:02.663126 | 2022-11-28T14:58:34 | 2022-11-28T14:58:34 | 204,721,695 | 5 | 23 | null | 2019-12-06T01:15:11 | 2019-08-27T14:30:27 | Python | UTF-8 | Python | false | false | 100 | py | # Printing hello world
def main():
print("Hello World")
if __name__ == '__main__':
main()
| [
"pradeepjaladi@gmail.com"
] | pradeepjaladi@gmail.com |
612247c1e53605ffa741a2fd8c545e5aee1047b8 | 1c2a9ce62301d5342113f2fdea8faefe807877c3 | /weekly/models.py | 95cda273c45b342928bebd15c878c21b9bdd4218 | [] | no_license | Jillelanglas/weekly | 782c03595118bb110c6d4ef3cda182d4b750ce30 | b4b5bd373b7b9a07198c1354ea2f9a7854ffa75b | refs/heads/master | 2021-01-15T23:07:08.495235 | 2013-10-05T18:01:51 | 2013-10-05T18:01:51 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,524 | py | from weekly import db
import cryptacular.bcrypt
import datetime
import mongoengine
from flask import url_for
from misaka import Markdown, HtmlRenderer
rndr = HtmlRenderer()
md = Markdown(rndr)
crypt = cryptacular.bcrypt.BCRYPTPasswordManager()
class User(db.Document):
_password = db.StringField(max_length=1023, required=True)
username = db.StringField(max_length=32, min_length=3, unique=True)
name = db.StringField(max_length=32, min_length=3, unique=True)
team = db.ReferenceField('Team')
major = db.ReferenceField('Major')
email = db.StringField(required=True)
admin = db.BooleanField(default=False)
active = db.BooleanField(default=False)
_type = db.IntField(min_value=0, max_value=3)
@property
def type(self):
if self._type == 0:
return 'Volunteer'
elif self._type == 1:
return 'Senior'
elif self._type == 2:
return 'Alumni'
else:
return 'Other'
@property
def password(self):
return self._password
@password.setter
def password(self, val):
self._password = unicode(crypt.encode(val))
def check_password(self, password):
return crypt.check(self._password, password)
def is_authenticated(self):
return True
def is_active(self):
return self.active
def is_anonymous(self):
return False
def get_id(self):
return unicode(self.id)
def __repr__(self):
return '<User %r>' % (self.nickname)
class Comment(db.EmbeddedDocument):
body = db.StringField(min_length=10)
user = db.ReferenceField(User, required=True)
time = db.DateTimeField()
@property
def md_body(self):
return md.render(self.body)
class Post(db.Document):
id = db.ObjectIdField()
body = db.StringField(min_length=10)
timestamp = db.DateTimeField(default=datetime.datetime.now())
year = db.IntField(required=True)
week = db.IntField(required=True)
user = db.ReferenceField(User, required=True)
comments = db.ListField(db.EmbeddedDocumentField(Comment))
@property
def md_body(self):
return md.render(self.body)
@classmethod
def next_week(self, week=None, year=None):
now = datetime.datetime.now().isocalendar()
if not week:
week = now[1] - 1
if not year:
year = now[0]
if week == 52:
year += 1
week = 0
else:
week += 1
return url_for('index', week=week, year=year)
@classmethod
def prev_week(self, week=None, year=None):
now = datetime.datetime.now().isocalendar()
if not week:
week = now[1] - 1
if not year:
year = now[0]
if week == 0:
year -= 1
week = 52
else:
week -= 1
return url_for('index', week=week, year=year)
def add_comment(self, user, body):
comment = Comment(user=user,
body=body,
time=datetime.datetime.now())
self.comments.append(comment)
self.save()
class Team(db.Document):
id = db.ObjectIdField()
text = db.StringField()
def __str__(self):
return self.text
def users(self):
return User.objects(team=self, _type=1)
class Major(db.Document):
key = db.StringField(max_length=5, primary_key=True)
text = db.StringField()
def __str__(self):
return self.text
| [
"isaac@simpload.com"
] | isaac@simpload.com |
68d7f32302c77679396fd2327a7d8a2eae33ffc6 | ebb288731179ca6d7e92afa798ffd272e8ff2af5 | /aggregator/migrations/0007_auto_20170605_1110.py | 87fd8ee724361115d8e420112cd091af170c43f6 | [] | no_license | anirudhshenoy/projectabc | 1d4398cdeb79f460b63e6210275b4611eb6faf43 | d65d6fe6a0cfa5a95fd5ec17dec0192e97efcaca | refs/heads/master | 2021-01-23T04:58:44.797378 | 2017-06-05T09:31:41 | 2017-06-05T09:31:41 | 92,945,924 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 2,025 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.11.1 on 2017-06-05 05:40
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('aggregator', '0006_auto_20170605_1050'),
]
operations = [
migrations.RemoveField(
model_name='chapterdetail',
name='chapter',
),
migrations.RemoveField(
model_name='content',
name='chapterNumber',
),
migrations.RemoveField(
model_name='content',
name='grade',
),
migrations.RemoveField(
model_name='content',
name='subject',
),
migrations.AddField(
model_name='chapterdetail',
name='chapterNumber',
field=models.CharField(default='NA', max_length=2),
),
migrations.AddField(
model_name='chapterdetail',
name='chapterTitle',
field=models.CharField(default='Title', max_length=300),
),
migrations.AddField(
model_name='chapterdetail',
name='grade',
field=models.CharField(choices=[('5th', '5th'), ('6th', '6th'), ('7th', '7th'), ('8th', '8th')], default='5th', max_length=3),
),
migrations.AddField(
model_name='chapterdetail',
name='subject',
field=models.CharField(choices=[('Science', 'Science'), ('Mathematics', 'Mathematics'), ('English', 'English')], default='Science', max_length=20),
),
migrations.AddField(
model_name='content',
name='chapter',
field=models.ForeignKey(default=1, on_delete=django.db.models.deletion.CASCADE, to='aggregator.chapterDetail'),
),
migrations.AlterField(
model_name='content',
name='contentTitle',
field=models.CharField(max_length=1000),
),
]
| [
"anirudhshenoy92@gmail.com"
] | anirudhshenoy92@gmail.com |
aec4627704796f4d5e56893bda32a06a151ead98 | 1260be5f711b389385a34bf830c8382c87af8367 | /envWiki/lib/python3.6/site-packages/jsonable/jsonable.py | 88ef98970ed6e75df0e9990a5c772561f36a806a | [] | no_license | lucas0/WikiCheck | a1bf0d2807a5c27b75817fd846ba15b9e301cc93 | b8fc309cf1ce3f1db3f760347a592c0d0175a1c9 | refs/heads/master | 2020-09-19T16:11:23.544697 | 2019-11-26T17:01:37 | 2019-11-26T17:01:37 | 224,243,729 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,218 | py | import copy
from . import instance
from .self_constructor import SelfConstructor
JSON_TYPES = {str, int, float, type(None), bool}
class JSONable(SelfConstructor):
"""
Implements a simple class interface for trivially JSONable objects.
:Example:
>>> from jsonable import JSONable
>>>
>>>
>>> class Fruit(JSONable):
... __slots__ = ('type', 'weight')
...
... def initialize(self, type, weight):
... self.type = str(type)
... self.weight = float(weight)
...
>>> class Pie(JSONable):
... __slots__ = ('fruit',)
...
... def initialize(self, fruit):
... self.fruit = [Fruit(f) for f in fruit]
...
...
>>> pie = Pie([Fruit('apple', 10.3), Fruit('cherry', 2)])
>>>
>>> doc = pie.to_json()
>>>
>>> pie == Pie(doc)
True
"""
def __new__(cls, *args, **kwargs):
if len(args) == 1 and len(kwargs) == 0 and isinstance(args[0], dict):
return cls.from_json(args[0])
else:
return super().__new__(cls, *args, **kwargs)
def __eq__(self, other):
if other == None: return False
try:
for key in instance.slots_keys(self):
if getattr(self, key) != getattr(other, key): return False
return True
except KeyError or AttributeError:
return False
def __neq__(self, other):
return not self.__eq__(other)
def __str__(self): return self.__repr__()
def __repr__(self):
return instance.slots_repr(self)
def to_json(self):
return {k:self._to_json(v) for k, v in instance.slots_items(self)}
@classmethod
def _to_json(cls, value):
if type(value) in JSON_TYPES:
return value
elif hasattr(value, "to_json"):
return value.to_json()
elif isinstance(value, list):
return [cls._to_json(v) for v in value]
elif isinstance(value, dict):
return {str(k):cls._to_json(v) for k,v in value.items()}
else:
raise TypeError("{0} is not json serializable.".format(type(value)))
def __getstate__(self): return self.to_json()
def __getnewargs__(self):
return (self.to_json(), )
#def __setstate__(self, doc): return self.intialize(**doc)
@classmethod
def from_json(cls, doc):
return cls(**doc)
_from_json = from_json
class AbstractJSONable(JSONable):
"""
Implements a simple JSONable datastructure for abstract classes.
:Example:
>>> from jsonable import JSONable, AbstractJSONable
>>>
>>> class Bowl(JSONable):
... __slots__ = ('fruit',)
... def initialize(self, fruit):
... self.fruit = [Fruit(f) for f in fruit]
...
>>>
>>>
>>> class Fruit(AbstractJSONable):
... __slots__ = ('weight',)
... def initialize(self, weight):
... self.weight = float(weight) # lbs
...
>>>
>>> class Apple(Fruit):
... __slots__ = ('variety',)
... def initialize(self, weight, variety):
... super().initialize(weight)
... self.variety = str(variety)
...
>>> Fruit.register(Apple)
>>>
>>> class Orange(Fruit):
... __slots__ = ('radius',)
... def initialize(self, weight, radius):
... super().initialize(weight)
... self.radius = float(radius) # in
...
>>> Fruit.register(Orange)
>>>
>>> orange = Orange(10.1, 2.5)
>>>
>>> apple = Apple(9.2, "Honey Crisp")
>>>
>>> bowl = Bowl([apple, orange])
>>>
>>> doc = bowl.to_json()
>>>
>>> bowl == Bowl(doc)
True
"""
__slots__ = tuple()
REGISTERED_SUB_CLASSES = {}
CLASS_NAME_KEY = "__class__"
def to_json(self):
doc = super().to_json()
doc[self.CLASS_NAME_KEY] = self.__class__.__name__
return doc
@classmethod
def from_json(cls, doc):
if cls.CLASS_NAME_KEY in doc:
class_name = doc[cls.CLASS_NAME_KEY]
if class_name in cls.REGISTERED_SUB_CLASSES:
SubClass = cls.REGISTERED_SUB_CLASSES[class_name]
elif class_name == cls.__name__:
SubClass = cls
else:
raise KeyError(str(class_name) +
" is not a recognized subclass of " +
cls.__name__)
new_doc = copy.copy(doc)
del new_doc[cls.CLASS_NAME_KEY]
return SubClass.from_json(new_doc)
else:
return cls._from_json(doc)
@classmethod
def get(cls, class_name):
return cls.REGISTERED_SUB_CLASSES[class_name]
@classmethod
def register(cls, SubClass):
cls.REGISTERED_SUB_CLASSES[SubClass.__name__] = SubClass
| [
"lucas.ccomp@gmail.com"
] | lucas.ccomp@gmail.com |
91844c1ed6cc7e36ae4119c9586f5fb82f28822b | e204cdd8a38a247aeac3d07f6cce6822472bdcc5 | /.history/app_test_django/models_20201116133107.py | 2523c8d06c874130fa411ddfea0a2aa8bcbbfe7e | [] | no_license | steven-halla/python-test | 388ad8386662ad5ce5c1a0976d9f054499dc741b | 0b760a47d154078002c0272ed1204a94721c802a | refs/heads/master | 2023-04-08T03:40:00.453977 | 2021-04-09T19:12:29 | 2021-04-09T19:12:29 | 354,122,365 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,940 | py | from django.db import models
import re
class UserManager(models.Manager):
def user_registration_validator(self, post_data):
errors = {}
EMAIL_REGEX = re.compile(
r'^[a-zA-Z0-9.+_-]+@[a-zA-Z0-9._-]+\.[a-zA-Z]+$')
if len(post_data['first_name']) < 3:
errors['first_name'] = "First name must be 3 characters"
if post_data['first_name'].isalpha() == False:
errors['first_name'] = "letters only"
if len(post_data['last_name']) < 3:
errors['last_name'] = "Last name must be 3 characters"
if post_data['last_name'].isalpha() == False:
errors['last_name'] = "letters only"
if len(post_data['email']) < 8:
errors['email'] = "Email must contain 8 characters"
#if post_data['email'].Books.objects.filter(title=post_data) == True:
# errors['email'] ="this email already exist in database"
if post_data['email'].find("@") == -1:
errors['email'] = "email must contain @ and .com"
if post_data['email'].find(".com") == -1:
errors['email'] = "email must contain @ and .com"
# test whether a field matches the pattern
if not EMAIL_REGEX.match(post_data['email']):
errors['email'] = "Invalid email address!"
if post_data['password'] != post_data['confirm_password']:
errors['pass_match'] = "password must match confirm password"
if len(post_data['password']) < 8:
errors['pass_length'] = "password must be longer than 8 characters"
return errors
class User(models.Model):
first_name = models.CharField(max_length=20)
last_name = models.CharField(max_length=20)
email = models.CharField(max_length=20)
password = models.CharField(max_length=20)
created_at = models.DateTimeField(auto_now_add=True)
updated_at = models.DateTimeField(auto_now=True)
objects = UserManager()
class TripManager(models.Manager):
def add_trip_validator(self, post_data):
errors = {}
if len(post_data['destination']) < 2:
errors['title'] = "destination name must be 2 characters"
if len(post_data['startdate']) < 1:
errors['title'] = "start date needs input"
if len(post_data['enddate']) < 1:
errors['desc'] = "end date needs input"
if len(post_data['plan']) < 5:
errors['desc'] = "plan must be 5 characters"
return errors
class Trip(models.Model):
destination = models.CharField(max_length=20)
startdate = models.DateTimeField()
enddate = models.DateTimeField()
plan = models.CharField(max_length=30)
uploaded_by = models.ForeignKey(User, related_name="trip_uploaded", on_delete=models.CASCADE)
created_at = models.DateTimeField(auto_now_add=True)
updated_at = models.DateTimeField(auto_now=True)
objects=TripManager()
| [
"69405488+steven-halla@users.noreply.github.com"
] | 69405488+steven-halla@users.noreply.github.com |
999058a6a97cf347926a34d59d7b8b61d67917c9 | a0602b11ef9ea422d3c331d07678314933f99331 | /model/Optimizer.py | c9bdea5104258fc7795da695f67619dd410bd402 | [] | no_license | ElijhaLee2/LinkinNet | 7791eecd6ca5ed44ea07fbe375ecb3fdb5c35f2a | 0893209a451ba1ad30f6bf6d17ca95798e45d3e7 | refs/heads/master | 2021-07-21T03:13:01.138991 | 2017-10-24T09:25:06 | 2017-10-24T09:25:06 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,172 | py | import tensorflow as tf
from model.hyperparameter import OPTIMIZER_DIS_HP, OPTIMIZER_GEN_HP
from other.config import BATCH_SIZE
from other.nn_func import optimize_loss
class DiscriminatorOptimizer:
def __init__(self, stack_to_train, name):
'''
:param models:
:param stack_to_train: a dict consist of g, dis_real, ...
:param name:
'''
with tf.name_scope(name) as scope:
self.stack_to_train = stack_to_train
self.variables_to_train = self._get_variables()
self.loss = self._get_loss()
self.optmzr = self._get_optimizer(name)
self._summary_dis()
# self.merge = tf.summary.merge(tf.get_collection(tf.GraphKeys.SUMMARIES, scope=scope))
self.summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope=scope)
def _get_loss(self):
lambda_ = OPTIMIZER_DIS_HP['lambda']
dis_fake = self.stack_to_train['dis_fake']
dis_real = self.stack_to_train['dis_real']
dis_wrong = self.stack_to_train['dis_wrong']
dis_penalty = self.stack_to_train['dis_penalty']
# w distance
neg_w_dis = dis_fake.score - dis_real.score
tf.summary.scalar('w_dis', -tf.reduce_mean(neg_w_dis))
[gradients_img, gradients_emb] = tf.gradients(dis_penalty.score,
[dis_penalty.image_input, dis_penalty.emb_input],
name='grad_img_emb')
tf.summary.histogram('gradients_img', gradients_img)
tf.summary.histogram('gradients_emb', gradients_emb)
norm2_img_square = tf.reduce_sum(tf.square(gradients_img), reduction_indices=[1, 2, 3])
norm2_emb_square = tf.reduce_sum(tf.square(gradients_emb), reduction_indices=[1])
norm2 = tf.sqrt(norm2_img_square + norm2_emb_square)
norm2_img = tf.sqrt(norm2_img_square)
norm2_emb = tf.sqrt(norm2_emb_square)
tf.summary.scalar('norm2', tf.reduce_mean(norm2))
tf.summary.scalar('norm2_img', tf.reduce_mean(norm2_img))
tf.summary.scalar('norm2_emb', tf.reduce_mean(norm2_emb))
if IS_ONE_NORM:
gp = tf.reduce_mean(tf.square(norm2 - 1))
else:
gp = tf.reduce_mean(tf.square(norm2_img - 1)) + tf.reduce_mean(tf.square(norm2_emb - 1))
loss = (tf.reduce_mean(dis_fake.score) + tf.reduce_mean(dis_wrong.score)) / 2 \
- tf.reduce_mean(dis_real.score) \
+ lambda_ * gp
return loss
def _get_optimizer(self, name):
learning_rate = OPTIMIZER_DIS_HP['learning_rate']
optimizer = OPTIMIZER_DIS_HP['optimizer']
step = tf.Variable(0, trainable=False, name=name + '/global_step')
optmzr = optimize_loss(self.loss, step, learning_rate, optimizer,
variables=self.variables_to_train)
ret = optmzr
return ret
def _get_variables(self):
model = self.stack_to_train['dis_real']
return model.trainable_variables
def _summary_dis(self, ):
dis_real = self.stack_to_train['dis_real']
dis_fake = self.stack_to_train['dis_fake']
real_pic = self.one_hot_inverse(dis_real.image_input) if dis_real.variable_scope_name.find(
'seg') != -1 else dis_real.image_input
fake_pic = self.one_hot_inverse(dis_fake.image_input) if dis_fake.variable_scope_name.find(
'seg') != -1 else dis_fake.image_input
concat = tf.concat([real_pic, fake_pic], axis=2)
tf.summary.image('real_fake', concat, max_outputs=BATCH_SIZE)
tf.summary.histogram('real_image', real_pic)
tf.summary.histogram('fake_image', fake_pic)
def one_hot_inverse(self, pic):
return \
tf.cast(
tf.expand_dims(
tf.arg_max(pic, -1),
axis=3),
tf.float32)
class GeneratorOptimizer:
def __init__(self, stack_to_train, name):
with tf.name_scope(name) as scope:
self.stack_to_train = stack_to_train
self.variables_to_train = self._get_variables()
self.loss = self._get_loss()
self.optmzr = self._get_optimizer(name)
# self.merge = tf.summary.merge(tf.get_collection(tf.GraphKeys.SUMMARIES, scope=scope))
self.summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope=scope)
def _get_loss(self):
dis_fake = self.stack_to_train['dis_fake']
loss_dis = -tf.reduce_mean(dis_fake.score)
# tf.summary.scalar('loss_dis', loss_dis)
loss = loss_dis
return loss
def _get_optimizer(self, name):
learning_rate = OPTIMIZER_GEN_HP['learning_rate']
optimizer = OPTIMIZER_GEN_HP['optimizer']
step = tf.Variable(0, trainable=False, name=name + '/global_step')
optmzr = optimize_loss(self.loss, step, learning_rate, optimizer,
variables=self.variables_to_train)
return optmzr
def _get_variables(self):
model = self.stack_to_train['gen']
return model.trainable_variables
| [
"elijhalee1994@gmail.com"
] | elijhalee1994@gmail.com |
902b8d163053965b0fd5ccb0bccc4093f6735a82 | 0adf94fc39a02018165b62e93dd83edddd041230 | /.history/Jobs/views_20190225164613.py | 81e0cf17f2f0704d47a0e7fa8441b3d22cbb48ad | [] | no_license | SabitDeepto/BrJobs | 1e3baa143331cf46b9c70911c6644d1efd4fffd6 | 1a458c8c667f8093a2325d963e5542655467c7aa | refs/heads/master | 2020-04-24T08:02:26.350007 | 2019-03-17T05:53:30 | 2019-03-17T05:53:30 | 171,818,024 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,304 | py | from django.contrib.auth.forms import UserCreationForm
from django.shortcuts import redirect, render
from django.urls import reverse_lazy
from django.views import generic
from .forms import UserForm, ProfileForm
from django.contrib import messages
from django.db.models import Q
from django.shortcuts import get_object_or_404, render, render_to_response
from .forms import JobPostForm
from .models import JobPost
def home(request):
post = JobPost.objects.all()
return render(request, 'basic/index.html', {'post': post})
def single_post(request, post_id):
post = JobPost.objects.get(pk=post_id)
return render(request, 'basic/detail.html', {'post': post})
def jobpost(request):
form = JobPostForm(request.POST, request.FILES)
if form.is_valid():
form.save()
return redirect('home')
return render(request, 'basic/client-job.html', {'form': form})
def update_profile(request):
if request.method == 'POST':
user_form = UserForm(request.POST, instance=request.user)
profile_form = ProfileForm(request.POST, instance=request.user.profile)
if user_form.is_valid() and profile_form.is_valid():
user_form.save()
profile_form.save()
messages.success(request, ('Your profile was successfully updated!'))
# return redirect('settings:profile')
else:
messages.error(request, ('Please correct the error below.'))
else:
user_form = UserForm(instance=request.user)
profile_form = ProfileForm(instance=request.user.profile)
return render(request, 'basic/test.html', {
'user_form': user_form,
'profile_form': profile_form
})
def searchposts(request):
if request.method == 'GET':
query = request.GET.get('q')
submitbutton = request.GET.get('submit')
if query is not None:
lookups = Q(title__icontains=query) | Q(detail__icontains=query)
results = Blog.objects.filter(lookups).distinct()
context = {'results': results,
'submitbutton': submitbutton}
return render(request, 'blog/blog_view.html', context)
else:
return render(request, 'blog/blog_view.html')
else:
return render(request, 'blog/blog_view.html')
| [
"deepto69@gmail.com"
] | deepto69@gmail.com |
0cc4452cd23425e09b958bb992224f2bbc531d8d | 38eab4b245f5a6bfb72e67068ccc8d1060a527b9 | /higherLower.py | 70aa49b7e98848a7d2305531b41e2e2dee114514 | [] | no_license | DimitrisPatiniotis/cardGamesPython | 7705948e1946069bebc16c1c18d02cf3fc10bb71 | 8f1ec3ffdc81aa7fd6f317169d933ba7d53b655d | refs/heads/main | 2023-01-12T21:17:10.091733 | 2020-11-17T06:38:28 | 2020-11-17T06:38:28 | 312,189,987 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,167 | py | from cardGameClasses import Card, Deck
game_on = True
score = 0
while game_on:
usedDeck = Deck()
usedDeck.shuffle()
dealtCard = usedDeck.deal_one()
print("The current card is ", dealtCard)
print("Your score is " + str(score))
userInput = input("Higher or lower?")
while userInput != 'h' and userInput != 'l':
print('Please enter a valid input (h for higher or l for lower)')
userInput = input("Higher or lower?")
if userInput == 'h' or userInput == 'l':
break
nextCard = usedDeck.deal_one()
print("The next card is ", nextCard)
if dealtCard.value == nextCard.value:
print("It's a tie!")
elif dealtCard.value > nextCard.value:
if userInput == 'h':
print('Wrong answer! Your final score is ' + str(score))
game_on = False
elif userInput == 'l':
print("Correct guess!")
score += 1
else:
if userInput == 'h':
print("Correct guess!")
score += 1
elif userInput == 'l':
print('Wrong answer! Your final score is ' + str(score))
game_on = False | [
"dimpatspy@gmail.com"
] | dimpatspy@gmail.com |
7293cd14ce1bf9222eeaeb863917fdc025928ff6 | 1078d0c9353e51b3e8b1c0ca8d96029dc373d52b | /BMIcalculator.py | c5fefe69abc956d84208dd8d6fd471263945c8c5 | [] | no_license | RevanthPro/Simple-BMI-calculator-in-Python | e4ccd2731f9e67195fbcd3f3a0bb34e9d84c04d8 | f8b70a0b6e7917806a1208d8c7818ef7bb3ee979 | refs/heads/main | 2023-05-04T15:45:05.423262 | 2021-05-30T10:32:07 | 2021-05-30T10:32:07 | 372,181,077 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 923 | py | #Taking Input from the user
name = input("Hi, What's your name?")
height = float(input("What's your height? (In metres)"))
weight = float(input("What's your weight? (In kg)"))
#Our next task is to identify the BMI of the user. BMI's formula is (kg/metres squared)
#Converting the value of our user's height from metres to metres squared
height = height * height # or height ** 2 (aka) height to the power of 2
#Calculating the BMI
BMI = weight/height
#Knowing if the person is overweight or underweight or normal weight
'''
If the person's BMI is below 18.5 then he is underweight
Or if the person's BMI is between 18.5 and 24.9 then the person's BMI is normal
Or if the person's BMI is above 24.9 then the person is overweight'''
#Coding if statements
if BMI < 18.5:
print(name + " is underweight")
elif BMI <= 24.9:
print(name + " is normal")
else:
print(name + " is overweight") | [
"noreply@github.com"
] | noreply@github.com |
38976f3a1ff363148a83b6a5aa39ef0ca6fe24c3 | 6fd32a62d7a560f810214319c644dbd665cb2a26 | /tests/client_test.py | 9b39df086f823f68477978cf3e88a1ffc1bfa469 | [
"Apache-2.0"
] | permissive | yuce/hazelcast-python-client-kerberos | 9d493d071621c46fd3c0a79f744e5161d88fcb73 | f00181b9264eec1887ad7ff7f860aadac91e8e6f | refs/heads/main | 2023-07-24T04:30:55.087050 | 2021-08-31T13:31:21 | 2021-08-31T13:31:21 | 401,269,308 | 0 | 0 | Apache-2.0 | 2021-08-30T08:23:46 | 2021-08-30T08:23:45 | null | UTF-8 | Python | false | false | 1,144 | py | import logging
import unittest
import hazelcast
import hzkerberos
from .util import default_keytab, Address
def customize_logger(name):
lg = logging.getLogger(name)
lg.setLevel(logging.DEBUG)
handler = logging.StreamHandler()
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
handler.setFormatter(formatter)
lg.addHandler(handler)
customize_logger("hazelcast")
customize_logger("hzkerberos")
class ClientTest(unittest.TestCase):
default_address = None
@classmethod
def setUpClass(cls):
cls.default_address = Address(host="hz1", resolve=True)
def test_token(self):
token_provider = hzkerberos.TokenProvider(
principal="jkey@EXAMPLE.COM", keytab=default_keytab()
)
addr = "%s:5701" % self.default_address.host
client = hazelcast.HazelcastClient(
cluster_members=[addr],
token_provider=token_provider,
cluster_connect_timeout=5,
)
m = client.get_map("auth-map").blocking()
m.put("k1", "v1")
v = m.get("k1")
self.assertEqual("v1", v)
| [
"noreply@github.com"
] | noreply@github.com |
1a04a1f887d81a5162e78550366bc82405a7caee | 9bd80dfa6f12613b887445c71d5e29a0d24cb0f7 | /modi.py | ebcc5f8c2cbe9fda6fc679f662dee830aa011cfc | [] | no_license | Tealephant/dokohelper | b9bc21d09b178ec6baf005220bed430ff0927001 | 4dab7a4f0193c46108174326dae114e77f743b3e | refs/heads/main | 2023-03-26T14:40:14.506225 | 2021-03-25T18:59:57 | 2021-03-25T18:59:57 | 346,080,990 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,094 | py | '''
Saves all the Gamemodes with the ranks and other constants
'''
#Fallback Playernames
FALLBACKNAMES = ["Spieler 1", "Spieler 2", "Spieler 3", "Spieler 4"]
#for creating all cards in a game
SUITS = ['C', 'S', 'H', 'D']
VALS = ['A', '0', 'K', '9', 'Q', 'J']
FULLDECK = [suit + val for suit in SUITS for val in VALS]
#ranks if no card is trump
RANK_VAL = ["A", "0", "K", "Q", "J", "9"]
#ranks used in multiple gamemodes
RANKNORMAL = ["H0", "CQ", "SQ", "HQ", "DQ", "CJ", "SJ", "HJ", "DJ", "DA", "D0", "DK", "D9"]
RANKNORMALS = ["DA", "H0", "CQ", "SQ", "HQ", "DQ", "CJ", "SJ", "HJ", "DJ", "D0", "DK", "D9"]
#saves the ranking of Trumpcards for the modes
RANKS = {
"normal" : RANKNORMAL,
"normals" : RANKNORMALS,
"hochzeit" : RANKNORMAL,
"hochzeits" : RANKNORMALS,
"armut" : RANKNORMAL,
"armuts" : RANKNORMALS,
"karo" : RANKNORMAL,
"karos" : RANKNORMALS,
"herz" : ["H0", "CQ", "SQ", "HQ", "DQ", "CJ", "SJ", "HJ", "DJ", "HA", "HK", "H9"],
"herzs" : ["HA", "H0", "CQ", "SQ", "HQ", "DQ", "CJ", "SJ", "HJ", "DJ", "HK", "H9"],
"pik" : ["H0", "CQ", "SQ", "HQ", "DQ", "CJ", "SJ", "HJ", "DJ", "SA", "S0", "SK", "S9"],
"piks" : ["SA", "H0", "CQ", "SQ", "HQ", "DQ", "CJ", "SJ", "HJ", "DJ", "S0", "SK", "S9"],
"kreuz" : ["H0", "CQ", "SQ", "HQ", "DQ", "CJ", "SJ", "HJ", "DJ", "CA", "C0", "CK", "C9"],
"kreuzs" : ["CA", "H0", "CQ", "SQ", "HQ", "DQ", "CJ", "SJ", "HJ", "DJ", "C0", "CK", "C9"],
"buben" : ["CJ", "SJ", "HJ", "DJ"],
"damen" : ["CQ", "SQ", "HQ", "DQ"],
"könige" : ["CK", "SK", "HK", "DK"],
"köhler" : ["CK", "SK", "HK", "DK", "CQ", "SQ", "HQ", "DQ", "CJ", "SJ", "HJ", "DJ"],
"fleischlos" : []
}
#saves the names for the Modi
MODI = {
"normal" : "Normalspiel",
"normals" : "Normalspiel (Schwein)",
"hochzeit" : "Hochzeit",
"hochzeits" : "Hochzeit (Schwein)",
"armut" : "Armut",
"armuts" : "Armut (Schwein)",
"karo" : "Karo-Solo",
"karos" : "Karo-Solo (Schwein)",
"herz" : "Herz-Solo",
"herzs" : "Herz-Solo (Schwein)",
"pik" : "Pik-Solo",
"piks" : "Pik-Solo (Schwein)",
"kreuz" : "Kreuz-Solo",
"kreuzs" : "Kreuz-Solo (Schwein",
"buben" : "Buben-Solo",
"damen" : "Damen-Solo",
"könige" : "König-Solo",
"köhler" : "Köhler-Solo",
"fleischlos" : "Fleischlos"
}
#Modes without Schwein for the mode selection menu
MODINOSCHWEIN = [
"normal", "hochzeit", "armut", "karo", "herz", "pik", "kreuz", "buben", "damen", "könige", "köhler", "fleischlos"
]
#saves which modes are soli, to immediately assign teams (assumes that solo-player always begins the round)
SOLI = [
"karo", "karos", "herz", "herzs", "pik", "piks", "kreuz", "kreuzs", "buben", "damen", "könige", "köhler", "fleischlos"
]
#saves in which modes the H10 has a special role
HEART10GAMES = [
"normal", "normals", "hochzeit", "hochzeits", "armut", "armuts", "karo", "karos", "herz", "herzs",
"pik", "piks", "kreuz", "kreuzs"
]
#saves in which modes schwein is available
SCHWEINAVAILABLE = [
"normal", "hochzeit", "armut", "karo", "herz", "pik", "kreuz"
] | [
"peter.stehr99@gmail.com"
] | peter.stehr99@gmail.com |
644bcacf0515b331c9bdaef4ab5c70d5990a3e12 | 0fd8c217f9c4279c3957d6e7da10c526b74a1d3b | /login_project/urls.py | 1b7b948e97da17c4cc05ab9d8588dd7ef66c72b2 | [] | no_license | dhananjaym11/python-django-login | 1613b45995f2e96242fd5176ac3e840ba5794e36 | 36381dd82a18f49c8d89c93244482673205c4899 | refs/heads/master | 2020-06-11T05:00:14.362760 | 2019-06-27T07:20:30 | 2019-06-27T07:20:30 | 193,856,322 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,006 | py | """login_project URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/2.2/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from django.contrib import admin
from django.urls import path , include
from userapp import views
urlpatterns = [
path('', views.index, name='index'),
path('user/', include('userapp.urls')),
# path('user-list', views.user_list_view, name='user_list'),
# path('user-form', views.user_form_view, name='user_form'),
path('admin/', admin.site.urls),
]
| [
"dhananjay.mane@xoriant.com"
] | dhananjay.mane@xoriant.com |
7d828beea5c587f81e6381a0c054f845e2b47ec5 | b98d9bca3a094bc2c3528db7cee612e3203183d4 | /proyectoRGMG/settings.py | 1d932825fed6c5ea4d521a4bceffb07053085c8b | [] | no_license | saenzjulian/RGMG-software | 6f62f071e679bc45396883c37ce1ab6f70b42418 | 8f8f0408c7cffcdaca02b7a727fffcd6ad2440e5 | refs/heads/master | 2022-11-23T18:59:47.608078 | 2020-08-05T20:05:34 | 2020-08-05T20:05:34 | 267,161,125 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,417 | py | """
Django settings for proyectoRGMG project.
Generated by 'django-admin startproject' using Django 3.0.3.
For more information on this file, see
https://docs.djangoproject.com/en/3.0/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/3.0/ref/settings/
"""
import os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'skzd%+yi$27(o)w2oc-wwc(6wg@h)^)#w@o(1yinb8_oa98ipe'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'rgmg',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'proyectoRGMG.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [os.path.join(BASE_DIR,'templates'),"C:/Users/Juan/Desktop/proyectoDjango/ProyectoRGMG/ProyectoRGMG/templates"],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'proyectoRGMG.wsgi.application'
# Database
# https://docs.djangoproject.com/en/3.0/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql_psycopg2',
'NAME': 'BaseDeDatosRGMG',
'USER': 'postgres',
'PASSWORD': '123',
'HOST': 'localhost',
'PORT': '5432',
}
}
# Password validation
# https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/3.0/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/3.0/howto/static-files/
STATIC_URL = '/static/'
STATICFILES_DIRS = [
os.path.join(BASE_DIR, "static"),
#'/var/www/static/',
]
| [
"julian@treecode.co"
] | julian@treecode.co |
70c8c8a64af43dce14259867c8b7bb96acde538f | cca4cd05531c42cf2440f8de6d9372aab5ed9f21 | /AYED/AYED 2020-1/religions.py | 281a0bc42e349199a86f9cf5cf3cc431abbf9725 | [] | no_license | CamiloCastiblanco/AYED-AYPR | 35c84bfb41c34e645f5b7a60945e682e7bf849e8 | 69952a893233d3d4dc69f31ce678a607bd2e6a0b | refs/heads/master | 2022-12-04T09:11:41.986471 | 2020-08-14T21:45:59 | 2020-08-14T21:45:59 | 287,629,283 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 826 | py | from sys import stdin
class DISTRASCHOLTEN:
def __init__(self,x):
self.data = [i for i in range(x+1)]
self.size = x
def union(self,i,j):
i = self.ans(i)
j = self.ans(j)
if j != i:
self.data[i] = j
self.size-=1
def ans(self,x):
if self.data[x] != x:
self.data[x] = self.ans(self.data[x])
return self.data[x]
def main():
z = 1
while -1:
x,y = list(map(int,stdin.readline().strip().split()))
if x == y == 0:
break
lista = DISTRASCHOLTEN(x)
for i in range(y):
j,k = list(map(int,stdin.readline().strip().split()))
lista.union(j,k)
p = lista.size
print('Case',str(z)+':',int(p))
z+=1
main()
| [
"noreply@github.com"
] | noreply@github.com |
19e9eb6c0f0128d8724b3f15dc2aeca49e1f211b | 2d921bb03eade0763ddb3a9cc5cb637730ecbde1 | /bdt/misassign_masses.py | 21339aff913311d7f6730d9ba3d5c46fd49fded9 | [] | no_license | rmanzoni/WTau3Mu | 10c57971b80f9769578284abd69009008901eea7 | 5ad336df976d5a1b39e4b516641661921b06ba20 | refs/heads/92X | 2021-01-18T15:10:41.887147 | 2019-05-09T12:48:00 | 2019-05-09T12:48:00 | 84,342,825 | 0 | 7 | null | 2018-07-19T09:08:19 | 2017-03-08T16:35:42 | Python | UTF-8 | Python | false | false | 4,883 | py | import ROOT
import root_pandas
import numpy as np
import pandas
import root_numpy
global m_k
global m_pi
m_k = 0.493677
m_pi = 0.13957061
# tree = ROOT.TChain('tree')
# tree.Add('/Users/manzoni/Documents/tau3mu2018/16april/ntuples/data_enriched_16apr2018v16.root')
print 'loading dataset...'
dataset = pandas.DataFrame(root_numpy.root2array(
'/Users/manzoni/Documents/tau3mu2018/16april/ntuples/data_enriched_16apr2018v16.root',
'tree',
# start=0,
# stop=100000,
)
)
print '\t...done'
mpp12_array = []
mpp13_array = []
mpp23_array = []
mkk12_array = []
mkk13_array = []
mkk23_array = []
mkp12_array = []
mkp13_array = []
mkp23_array = []
mpk12_array = []
mpk13_array = []
mpk23_array = []
mppp_array = []
mppk_array = []
mpkp_array = []
mkpp_array = []
mpkk_array = []
mkpk_array = []
mkkp_array = []
mkkk_array = []
# for i, ev in enumerate(tree):
for i in range(len(dataset)):
if i%10000 == 0:
print '========> processed %d/%d \tevents\t%.1f' %(i, len(dataset), float(i)/len(dataset))
# for i in range(10):
# k1p4 = ROOT.Math.LorentzVector('ROOT::Math::PtEtaPhiM4D<double>')(ev.mu1_pt, ev.mu1_eta, ev.mu1_phi, m_k )
# k2p4 = ROOT.Math.LorentzVector('ROOT::Math::PtEtaPhiM4D<double>')(ev.mu2_pt, ev.mu2_eta, ev.mu2_phi, m_k )
# k3p4 = ROOT.Math.LorentzVector('ROOT::Math::PtEtaPhiM4D<double>')(ev.mu3_pt, ev.mu3_eta, ev.mu3_phi, m_k )
#
# pi1p4 = ROOT.Math.LorentzVector('ROOT::Math::PtEtaPhiM4D<double>')(ev.mu1_pt, ev.mu1_eta, ev.mu1_phi, m_pi)
# pi2p4 = ROOT.Math.LorentzVector('ROOT::Math::PtEtaPhiM4D<double>')(ev.mu2_pt, ev.mu2_eta, ev.mu2_phi, m_pi)
# pi3p4 = ROOT.Math.LorentzVector('ROOT::Math::PtEtaPhiM4D<double>')(ev.mu3_pt, ev.mu3_eta, ev.mu3_phi, m_pi)
k1p4 = ROOT.Math.LorentzVector('ROOT::Math::PtEtaPhiM4D<double>')(dataset.mu1_refit_pt[i], dataset.mu1_refit_eta[i], dataset.mu1_refit_phi[i], m_k )
k2p4 = ROOT.Math.LorentzVector('ROOT::Math::PtEtaPhiM4D<double>')(dataset.mu2_refit_pt[i], dataset.mu2_refit_eta[i], dataset.mu2_refit_phi[i], m_k )
k3p4 = ROOT.Math.LorentzVector('ROOT::Math::PtEtaPhiM4D<double>')(dataset.mu3_refit_pt[i], dataset.mu3_refit_eta[i], dataset.mu3_refit_phi[i], m_k )
pi1p4 = ROOT.Math.LorentzVector('ROOT::Math::PtEtaPhiM4D<double>')(dataset.mu1_refit_pt[i], dataset.mu1_refit_eta[i], dataset.mu1_refit_phi[i], m_pi)
pi2p4 = ROOT.Math.LorentzVector('ROOT::Math::PtEtaPhiM4D<double>')(dataset.mu2_refit_pt[i], dataset.mu2_refit_eta[i], dataset.mu2_refit_phi[i], m_pi)
pi3p4 = ROOT.Math.LorentzVector('ROOT::Math::PtEtaPhiM4D<double>')(dataset.mu3_refit_pt[i], dataset.mu3_refit_eta[i], dataset.mu3_refit_phi[i], m_pi)
mpp12 = (pi1p4 + pi2p4).mass()
mpp13 = (pi1p4 + pi3p4).mass()
mpp23 = (pi2p4 + pi3p4).mass()
mkk12 = (k1p4 + k2p4).mass()
mkk13 = (k1p4 + k3p4).mass()
mkk23 = (k2p4 + k3p4).mass()
mkp12 = (k1p4 + pi2p4).mass()
mkp13 = (k1p4 + pi3p4).mass()
mkp23 = (k2p4 + pi3p4).mass()
mpk12 = (pi1p4 + k2p4).mass()
mpk13 = (pi1p4 + k3p4).mass()
mpk23 = (pi2p4 + k3p4).mass()
mppp = (pi1p4 + pi2p4 + pi3p4).mass()
mppk = (pi1p4 + pi2p4 + k3p4 ).mass()
mpkp = (pi1p4 + k2p4 + pi3p4).mass()
mkpp = (k1p4 + pi2p4 + pi3p4).mass()
mpkk = (pi1p4 + k2p4 + k3p4 ).mass()
mkpk = (k1p4 + pi2p4 + k3p4 ).mass()
mkkp = (k1p4 + k2p4 + pi3p4).mass()
mkkk = (k1p4 + k2p4 + k3p4 ).mass()
mpp12_array.append(mpp12)
mpp13_array.append(mpp13)
mpp23_array.append(mpp23)
mkk12_array.append(mkk12)
mkk13_array.append(mkk13)
mkk23_array.append(mkk23)
mkp12_array.append(mkp12)
mkp13_array.append(mkp13)
mkp23_array.append(mkp23)
mpk12_array.append(mpk12)
mpk13_array.append(mpk13)
mpk23_array.append(mpk23)
mppp_array .append(mppp )
mppk_array .append(mppk )
mpkp_array .append(mpkp )
mkpp_array .append(mkpp )
mpkk_array .append(mpkk )
mkpk_array .append(mkpk )
mkkp_array .append(mkkp )
mkkk_array .append(mkkk )
dataset['mpp12'] = mpp12_array
dataset['mpp13'] = mpp13_array
dataset['mpp23'] = mpp23_array
dataset['mkk12'] = mkk12_array
dataset['mkk13'] = mkk13_array
dataset['mkk23'] = mkk23_array
dataset['mkp12'] = mkp12_array
dataset['mkp13'] = mkp13_array
dataset['mkp23'] = mkp23_array
dataset['mpk12'] = mpk12_array
dataset['mpk13'] = mpk13_array
dataset['mpk23'] = mpk23_array
dataset['mppp'] = mppp_array
dataset['mppk'] = mppk_array
dataset['mpkp'] = mpkp_array
dataset['mkpp'] = mkpp_array
dataset['mpkk'] = mpkk_array
dataset['mkpk'] = mkpk_array
dataset['mkkp'] = mkkp_array
dataset['mkkk'] = mkkk_array
print 'staging dataset...'
dataset.to_root(
'/Users/manzoni/Documents/tau3mu2018/16april/ntuples/data_enriched_16apr2018v16_extra_masses.root',
key='tree',
store_index=False
)
print '\t...done'
| [
"riccardo.manzoni@cern.ch"
] | riccardo.manzoni@cern.ch |
91b34d7a48845d76c08dbfca409f01646a412e4e | 416cf107307e2447288d752be8bbf285015c20a6 | /node_modules/jsdom/node_modules/contextify/build/config.gypi | aff4a46eaf9b7183e197ebd95b7347bf823d4acc | [
"MIT"
] | permissive | 468/contentbot | ff5883328f159ae2a78aa3b60e3585296abc23d4 | c592718d32ac903abf0de8c9cde2ba0a239491a6 | refs/heads/master | 2020-06-24T03:44:52.805132 | 2019-07-25T14:16:38 | 2019-07-25T14:16:38 | 198,838,308 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,337 | gypi | # Do not edit. File was generated by node-gyp's "configure" step
{
"target_defaults": {
"cflags": [],
"default_configuration": "Release",
"defines": [],
"include_dirs": [],
"libraries": []
},
"variables": {
"clang": 0,
"gcc_version": 48,
"host_arch": "x64",
"icu_small": "false",
"node_install_npm": "true",
"node_prefix": "",
"node_shared_cares": "false",
"node_shared_http_parser": "false",
"node_shared_libuv": "false",
"node_shared_openssl": "false",
"node_shared_v8": "false",
"node_shared_zlib": "false",
"node_tag": "",
"node_use_dtrace": "false",
"node_use_etw": "false",
"node_use_mdb": "false",
"node_use_openssl": "true",
"node_use_perfctr": "false",
"openssl_no_asm": 0,
"python": "/usr/bin/python",
"target_arch": "x64",
"uv_library": "static_library",
"uv_parent_path": "/deps/uv/",
"uv_use_dtrace": "false",
"v8_enable_gdbjit": 0,
"v8_enable_i18n_support": 0,
"v8_no_strict_aliasing": 1,
"v8_optimized_debug": 0,
"v8_random_seed": 0,
"v8_use_snapshot": "true",
"want_separate_host_toolset": 0,
"nodedir": "/root/.node-gyp/0.12.0",
"copy_dev_lib": "true",
"standalone_static_library": 1,
"cache_lock_stale": "60000",
"sign_git_tag": "",
"user_agent": "npm/2.5.1 node/v0.12.0 linux x64",
"always_auth": "",
"bin_links": "true",
"key": "",
"description": "true",
"fetch_retries": "2",
"heading": "npm",
"init_version": "1.0.0",
"user": "",
"force": "",
"cache_min": "10",
"init_license": "ISC",
"editor": "vi",
"rollback": "true",
"cache_max": "Infinity",
"userconfig": "/root/.npmrc",
"engine_strict": "",
"init_author_name": "",
"init_author_url": "",
"tmp": "/tmp",
"depth": "Infinity",
"save_dev": "",
"usage": "",
"cafile": "",
"https_proxy": "",
"onload_script": "",
"rebuild_bundle": "true",
"save_bundle": "",
"shell": "/bin/bash",
"prefix": "/usr/local",
"browser": "",
"cache_lock_wait": "10000",
"registry": "https://registry.npmjs.org/",
"save_optional": "",
"scope": "",
"searchopts": "",
"versions": "",
"cache": "/root/.npm",
"ignore_scripts": "",
"searchsort": "name",
"version": "",
"local_address": "",
"viewer": "man",
"color": "true",
"fetch_retry_mintimeout": "10000",
"umask": "0022",
"fetch_retry_maxtimeout": "60000",
"message": "%s",
"ca": "",
"cert": "",
"global": "",
"link": "",
"access": "",
"save": "",
"unicode": "true",
"long": "",
"production": "",
"unsafe_perm": "",
"node_version": "0.12.0",
"tag": "latest",
"git_tag_version": "true",
"shrinkwrap": "true",
"fetch_retry_factor": "10",
"npat": "",
"proprietary_attribs": "true",
"save_exact": "",
"strict_ssl": "true",
"dev": "",
"globalconfig": "/usr/local/etc/npmrc",
"init_module": "/root/.npm-init.js",
"parseable": "",
"globalignorefile": "/usr/local/etc/npmignore",
"cache_lock_retries": "10",
"save_prefix": "^",
"group": "",
"init_author_email": "",
"searchexclude": "",
"git": "git",
"optional": "true",
"json": "",
"spin": "true"
}
}
| [
"alexandertaylor@alexandersmbp3.lan"
] | alexandertaylor@alexandersmbp3.lan |
976fba52d59ded334a47ea201d34a74a76c04a88 | d16398cb8d45b58019a9e1d1ff3b8105098d9db1 | /positional_index_search.py | 47f469b835d05ccbf14d9dfba00686169e0f6a25 | [] | no_license | Afnan1278/retrieval-model | 64fdd327cca42710c74ab37b6a4d9f5e82744b47 | 20c5b5b1135327a0494829636987f920e5941bb4 | refs/heads/master | 2023-02-26T23:52:10.812290 | 2021-02-03T16:49:02 | 2021-02-03T16:49:02 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,138 | py | import os
from nltk.stem import PorterStemmer
ps = PorterStemmer()
def reading_corpus():
stopfile = open("Stopword-List.txt", "r")
stopwords = [word for line in stopfile for word in line.split()]
dirr = os.getcwd()
file = os.path.join(dirr, "TrumpSpeechs")
punctuations = '''!()-[]{};:'",<>./?@#$%^&*_~'''
docid = 0
index_list = {}
while docid < 56:
file1 = os.path.join(file, "speech_"+str(docid)+".txt")
f = open(file1, "r")
filtered_words = preprocessing(f, stopwords, punctuations)
index_list = create_index(index_list, filtered_words, docid)
docid += 1
return index_list
def preprocessing(f, stopwords, punctuations):
no_punc = ' '
for line in f:
line = line.strip()
line = line.lower()
for char in line:
if char not in punctuations:
no_punc += char
words = no_punc.split(" ")
words = [ps.stem(word) for word in words]
filtered_words = [word for word in words if word not in stopwords]
filtered_words = [word for word in filtered_words if word != ""]
return filtered_words
def create_index(index_list, filtered_words, id):
for pos, term in enumerate(filtered_words):
if term not in index_list:
index_list[term] = []
index_list[term].append(1)
index_list[term].append({})
index_list[term][1][id] = [pos]
else:
index_list[term][0] += 1
if id not in index_list[term][1]:
index_list[term][1][id] = [pos]
else:
index_list[term][1][id].append(pos)
return index_list
dictionary = reading_corpus()
def pos_intersect(p1, p2, k):
answer = [] # will store documents which satisfy the condition
len1 = len(p1) # length of posting list of of first word
len2 = len(p2) # length of posting list of of second word
i = j = 0
docs1 = []
docs2 = []
for doc in p1.keys():
docs1.append(doc) # elements(docs) of posting list of first word
for doc in p2.keys():
docs2.append(doc) # elements(docs) of posting list of second word
while i != len1 and j != len2:
if docs1[i] == docs2[j]: # if both word occurs in same document
pp1 = p1[docs1[i]] # list of positions where 1st word occurs in doc i
pp2 = p2[docs2[j]] # list of positions where 2nd word occurs in doc j
plen1 = len(pp1) # length of position list of 1st word in doc i
plen2 = len(pp2) # length of position list of 2nd word in doc j
ii = jj = 0
while ii != plen1 and jj != plen2: # while (pp1 != nil and pp2 != nil)
if pp2[jj] - pp1[ii] == k: # if (pos(pp2) - pos(pp1) == k)
answer.append(docs1[i]) # add document in the answer
jj += 1 # pp2 <- next(pp2)
ii += 1 # pp1 <- next (pp1)
elif pp2[jj] > pp1[ii]: # else if (pos(pp2) > pos(pp1))
ii += 1
else:
jj += 1
i += 1 # next doc in position list of 1st word
j += 1 # next doc in position list of 2st wor
elif docs1[i] < docs2[j]: # else if (docID(p1) < docID(p2))
i += 1 # p1 <- next(p1)
else:
j += 1 # p2 <- next(p2)
return answer
def making_query(query):
print(query)
query_list1 = query.split(" ")
k = query_list1[1].split('/')
query_list = [word for word in query_list1]
print(query_list)
k1 = int(k[1])
query_list[0] = ps.stem(query_list[0])
k[0] = ps.stem(k[0])
answer = pos_intersect(dictionary[query_list[0]][1], dictionary[k[0]][1], k1 + 1)
answer = list(dict.fromkeys(answer)) # removing the duplicate documents from list
print(answer)
return answer
def main():
# dictionary = reading_corpus()
with open('pos-dictionary', 'w') as f:
for key, value in dictionary.items():
f.write('%s:%s\n' % (key, value))
answer = making_query()
if __name__ == '__main__':
main() | [
"afnananwer2@gmail.com"
] | afnananwer2@gmail.com |
933d56d5e99edaeae7ca06fd784066cc8ffc782d | 50aff0dba5bc0d2b8ab0daafd705a277e3149096 | /manage.py | 75d5ba10698c48cfaf7a046959b936b070c1c1e0 | [] | no_license | ueliojr/war_dm | 12004749443544bec02adf4ed81f33041922367d | b1d5530093dc5abed7c328f04ab0740c8db17947 | refs/heads/master | 2021-07-21T14:52:30.125562 | 2017-10-31T16:57:31 | 2017-10-31T16:57:31 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 809 | py | #!/usr/bin/env python
import os
import sys
if __name__ == "__main__":
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "war_demolay.settings")
try:
from django.core.management import execute_from_command_line
except ImportError:
# The above import may fail for some other reason. Ensure that the
# issue is really that Django is missing to avoid masking other
# exceptions on Python 2.
try:
import django
except ImportError:
raise ImportError(
"Couldn't import Django. Are you sure it's installed and "
"available on your PYTHONPATH environment variable? Did you "
"forget to activate a virtual environment?"
)
raise
execute_from_command_line(sys.argv)
| [
"jose.silvaneto@dce.ufpb.br"
] | jose.silvaneto@dce.ufpb.br |
c3dc0759d55c3bd863003122aa81d87bfceca785 | 7b9164e160b3f375c6ac28496617754aa8cf4a0b | /d_2018_1_4/longest_palindromic_substring.py | fdb379bbb425f7415c3d1d1b4ce0fbb2b3c40a2a | [] | no_license | WhalesZhong/leetcode_fun | 991854de2cb8075e1a092bd873d466a58db0216a | 2e30ee0c29bd49a276224022df1168cc433685d1 | refs/heads/master | 2021-05-04T14:28:23.325897 | 2018-02-10T12:13:00 | 2018-02-10T12:13:00 | 120,202,101 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,074 | py | # coding: utf-8
def longest_palindromic_substring(s):
"""
:param s:
:return:
"""
choice_part_start_idx = 0
is_find = False
max_length = 0
choice_part = ""
for idx, w in enumerate(s):
choice_part += w
# 如果下一个字符不符合则移动已选中的区间,记录长度
if choice_part != choice_part[::-1]:
length = idx - choice_part_start_idx + 1
if max_length < length:
max_length = length
choice_part = choice_part[1:]
choice_part_start_idx += 1
same_w_idx = choice_part.get(w)
if same_w_idx is None or same_w_idx < choice_part_start_idx:
choice_part[w] = idx
continue
length = idx - same_w_idx + 1
is_find = True
if max_length < length:
max_length = length
choice_part_start_idx = same_w_idx + 1
choice_part[w] = idx
if not is_find:
return ""
else:
return s[choice_part_start_idx-1:choice_part_start_idx-1+max_length] | [
"whaleszhong@gmail.com"
] | whaleszhong@gmail.com |
9667e86b4ca07c2e6716741e6cf0e9de4b7bdee6 | 4ad04de638ccfed398adb5496826c0d19e755d9e | /models/hr_contract_wage_type_period.py | 50c5042a81dece83a28e3107e503007f66523598 | [
"BSD-2-Clause"
] | permissive | aroodooteam/aro_hr_payroll | 2f399a0f2e45652d2791df48a95e5ad66a051d71 | dd95d500827566f1444e32760dda5b5b69a8906e | refs/heads/master | 2021-01-22T13:47:46.272642 | 2018-01-29T11:25:35 | 2018-01-29T11:25:35 | 100,686,534 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 629 | py | # -*- coding: utf-8 -*-
from openerp.osv import fields, osv
import time
import logging
logger = logging.getLogger(__name__)
# Contract wage type period name
class hr_contract_wage_type_period(osv.osv):
_name = 'hr.contract.wage.type.period'
_description = 'Wage Period'
_columns = {
'name': fields.char('Period Name', size=50,
required=True, select=True),
'factor_days': fields.float('Hours in the period',
digits=(12, 4), required=True,)
}
_defaults = {
'factor_days': 173.33
}
hr_contract_wage_type_period()
| [
"aroodoo@asus.aro"
] | aroodoo@asus.aro |
7b440b9c4a0ca5a1ae2bd7a5bbe81865d3bad1f6 | 3826ba3af7a09204a616f9ffa4f3d61261b4ff27 | /tools/utils.py | a004f696bb7c94129ecf2a2309d343fda61cdd75 | [
"MIT"
] | permissive | sunjiangbo/UEFI_RETool | cd019468acbcd141e631a966c8ba570d1ddc2434 | 1c866b44335d9eaf46bac2c37960d16c68d8b196 | refs/heads/master | 2021-03-02T02:28:45.832163 | 2020-03-05T15:43:10 | 2020-03-05T15:43:10 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,644 | py | # MIT License
#
# Copyright (c) 2018-2019 yeggor
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
IMAGE_FILE_MACHINE_IA64 = 0x8664
IMAGE_FILE_MACHINE_I386 = 0x014c
PE_OFFSET = 0x3c
def get_num_le(bytearr):
num_le = 0
for i in range(len(bytearr)):
num_le += bytearr[i] * pow(256, i)
return num_le
def get_machine_type(module_path):
with open(module_path, 'rb') as module:
data = module.read()
PE_POINTER = get_num_le(data[PE_OFFSET:PE_OFFSET+1:])
FH_POINTER = PE_POINTER + 4
machine_type = data[FH_POINTER:FH_POINTER+2:]
type_value = get_num_le(machine_type)
return type_value
| [
"vasilenko.yegor@gmail.com"
] | vasilenko.yegor@gmail.com |
14a8cf50a24e710cb32fd104efb1e7fdbb2df962 | 129d823eb0e783ff7208739eb69a786d61224a52 | /Problem005/main.py | 9b106467ac4475fd2a5983439989d348d9a883fe | [] | no_license | bomjic/StudyPython | dd493cdfc943da8c703ed5f7fd7584a466ec8623 | 8e850c6476c2dc3e0d9b8c996efa50e96fc76970 | refs/heads/master | 2020-07-05T14:44:38.843582 | 2018-05-03T19:14:38 | 2018-05-03T19:14:38 | 74,114,811 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 260 | py | dividor = 2520
flag = False
while flag != True:
for i in range(2, 21):
if dividor % i != 0:
break
if i == 20:
print (dividor)
flag = True
# if dividor % 1000000 == 0:
# print(dividor)
dividor += 2
| [
"mikhail.baikov@gmail.com"
] | mikhail.baikov@gmail.com |
49342dc0d723463fcc91fa44a273f4626921a157 | 6bb96c5116e7210f9abd4b7d0460eb85c95e99c6 | /毕设/tutorial/tutorial/mymiddlewares.py | 08a7073e44ccbed042249eda483de5a1c97ec2aa | [] | no_license | strange-jiong/graduation-project | 7940416c9a75a7337ef52dd988d95fc40628c178 | d5f0baaa7f23a7e246d671f6b2f0e8ee96520377 | refs/heads/master | 2021-01-10T03:04:46.517081 | 2017-06-14T02:02:17 | 2017-06-14T02:02:17 | 48,604,207 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 6,775 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Date : 2015-12-01 21:37:33
# @Author : jiong (447991103@qq.com)
# @Version : 0.1
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
from scrapy.exceptions import IgnoreRequest
from scrapy.contrib.downloadermiddleware import DownloaderMiddleware
import random
import base64
# from settings import PROXIES
# class RandomUserAgent(object):
# """Randomly rotate user agents based on a list of predefined ones"""
# def __init__(self, agents):
# self.agents = agents
# @classmethod
# def from_crawler(cls, crawler):
# return cls(crawler.settings.getlist('USER_AGENTS'))
# def process_request(self, request, spider):
# # print "**************************" + random.choice(self.agents)
# # request.headers.setdefault('User-Agent', random.choice(self.agents))
# class ProxyMiddleware(object):
# def process_request(self, request, spider):
# proxy = random.choice(PROXIES)
# if proxy['user_pass'] is not None:
# request.meta['proxy'] = "http://%s" % proxy['ip_port']
# encoded_user_pass = base64.encodestring(proxy['user_pass'])
# request.headers[
# 'Proxy-Authorization'] = 'Basic ' + encoded_user_pass
# print "**************ProxyMiddleware have pass************" + proxy['ip_port']
# else:
# print "**************ProxyMiddleware no pass************" + proxy['ip_port']
# request.meta['proxy'] = "http://%s" % proxy['ip_port']
"""避免被ban策略之一：使用useragent池。
使用注意：需在settings.py中进行相应的设置。
"""
# from scrapy import log
# import random
from scrapy.downloadermiddleware.useragent import UserAgentMiddleware
class RotateUserAgentMiddleware(UserAgentMiddleware):
# user_agent_list = [
# "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 "
# "(KHTML, like Gecko) Chrome/22.0.1207.1 Safari/537.1",
# "Mozilla/5.0 (X11; CrOS i686 2268.111.0) AppleWebKit/536.11 "
# "(KHTML, like Gecko) Chrome/20.0.1132.57 Safari/536.11",
# "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.6 "
# "(KHTML, like Gecko) Chrome/20.0.1092.0 Safari/536.6",
# "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.6 "
# "(KHTML, like Gecko) Chrome/20.0.1090.0 Safari/536.6",
# "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.1 "
# "(KHTML, like Gecko) Chrome/19.77.34.5 Safari/537.1",
# "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/536.5 "
# "(KHTML, like Gecko) Chrome/19.0.1084.9 Safari/536.5",
# "Mozilla/5.0 (Windows NT 6.0) AppleWebKit/536.5 "
# "(KHTML, like Gecko) Chrome/19.0.1084.36 Safari/536.5",
# "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 "
# "(KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3",
# "Mozilla/5.0 (Windows NT 5.1) AppleWebKit/536.3 "
# "(KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3",
# "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0) AppleWebKit/536.3 "
# "(KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3",
# "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 "
# "(KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3",
# "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 "
# "(KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3",
# "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 "
# "(KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3",
# "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 "
# "(KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3",
# "Mozilla/5.0 (Windows NT 6.1) AppleWebKit/536.3 "
# "(KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3",
# "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 "
# "(KHTML, like Gecko) Chrome/19.0.1061.0 Safari/536.3",
# "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/535.24 "
# "(KHTML, like Gecko) Chrome/19.0.1055.1 Safari/535.24",
# "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/535.24 "
# "(KHTML, like Gecko) Chrome/19.0.1055.1 Safari/535.24"
# ]
user_agent_list = [
"Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; AcooBrowser; .NET CLR 1.1.4322; .NET CLR 2.0.50727)",
"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.0; Acoo Browser; SLCC1; .NET CLR 2.0.50727; Media Center PC 5.0; .NET CLR 3.0.04506)",
"Mozilla/4.0 (compatible; MSIE 7.0; AOL 9.5; AOLBuild 4337.35; Windows NT 5.1; .NET CLR 1.1.4322; .NET CLR 2.0.50727)",
"Mozilla/5.0 (Windows; U; MSIE 9.0; Windows NT 9.0; en-US)",
"Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Win64; x64; Trident/5.0; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET CLR 2.0.50727; Media Center PC 6.0)",
"Mozilla/5.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0; WOW64; Trident/4.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET CLR 1.0.3705; .NET CLR 1.1.4322)",
"Mozilla/4.0 (compatible; MSIE 7.0b; Windows NT 5.2; .NET CLR 1.1.4322; .NET CLR 2.0.50727; InfoPath.2; .NET CLR 3.0.04506.30)",
"Mozilla/5.0 (Windows; U; Windows NT 5.1; zh-CN) AppleWebKit/523.15 (KHTML, like Gecko, Safari/419.3) Arora/0.3 (Change: 287 c9dfb30)",
"Mozilla/5.0 (X11; U; Linux; en-US) AppleWebKit/527+ (KHTML, like Gecko, Safari/419.3) Arora/0.6",
"Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.8.1.2pre) Gecko/20070215 K-Ninja/2.1.1",
"Mozilla/5.0 (Windows; U; Windows NT 5.1; zh-CN; rv:1.9) Gecko/20080705 Firefox/3.0 Kapiko/3.0",
"Mozilla/5.0 (X11; Linux i686; U;) Gecko/20070322 Kazehakase/0.4.5",
"Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.9.0.8) Gecko Fedora/1.9.0.8-1.fc10 Kazehakase/0.5.6",
"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.56 Safari/535.11",
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_3) AppleWebKit/535.20 (KHTML, like Gecko) Chrome/19.0.1036.7 Safari/535.20",
"Opera/9.80 (Macintosh; Intel Mac OS X 10.6.8; U; fr) Presto/2.9.168 Version/11.52",
]
def __init__(self, user_agent=''):
self.user_agent = user_agent
def process_request(self, request, spider):
ua = random.choice(self.user_agent_list)
print ua
# if ua:
# # 显示当前使用的useragent
# print "********Current UserAgent:%s************" % ua
# # 记录
# # log.msg('Current UserAgent: ' + ua, level='INFO')
request.headers.setdefault('User-Agent', ua)
# the default user_agent_list composes chrome,I E,firefox,Mozilla,opera,netscape
# for more user agent strings,you can find it in
# http://www.useragentstring.com/pages/useragentstring.php
# class CustomMiddlewares(DownloaderMiddleware):
# def process_response(self, request, response, spider):
# if len(response.body) == 100:
# return IgnoreRequest("body length == 100")
# else:
# return response
| [
"447991103@qq.com"
] | 447991103@qq.com |
bb4794cef121a09d6791df6d946f0b5ec141d088 | d52b11bd06b1d7aa8fe120a32b98cce9a1c27279 | /ML_Models_DT_Gaussian_LR/Models/GaussianNB.py | 214e78358bd825aef7c355b5ea44b0b2c1535b24 | [] | no_license | diksha16017/MLForBeginners | 5afd24b8f34b89860c37acec4ae91ff895716230 | 7c4724ee282d08a1a6673b1e919d9c139a213cf3 | refs/heads/master | 2020-03-15T19:25:10.706034 | 2018-05-05T19:19:37 | 2018-05-05T19:19:37 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,915 | py | import math
import numpy as np
from collections import Counter, defaultdict
import operator
# make sure this class id compatable with sklearn's GaussianNB
class GaussianNB(object):
def __init__(self):
# define all the model weights and state here
self.label_probabilities = dict()
self.mean_dict = {}
self.std_dict = {}
def fit(self, X, Y):
global labels
labels = np.unique(Y)
# print("labels: ", labels)
x_rows, x_cols = np.shape(X)
self.label_probabilities = self.compute_probabilities(Y)
# print("Label probs are:")
# print(self.label_probabilities)
separated_set = {}
for label in labels:
indices = np.where(Y == label)[0]
# print("l:", label)
# print(indices)
# Picking up the rows from training data whose Y == label and putting into a set called rows_with_label
rows_with_label = X[np.array(indices), 0:]
separated_set[label] = rows_with_label
# print(rows_with_label)
# print(separated_set[label])
# print('Separated instances: {0}').format(separated_set)
# Create a dictionary with class as keys and a list of mean of each attribute value in that class
for label in labels:
separate_class = separated_set[label]
# print("lbl:", label)
# print("normal: ", separate_class.shape)
# np.transpose()
separate_class = separate_class.transpose()
# print("transposed: ", separate_class.shape)
ht, wd = separate_class.shape
temp_mean = np.zeros(ht)
temp_std = np.zeros(ht)
index = 0
for s in separate_class:
temp_mean[index] += np.mean(s)
temp_std[index] += np.std(s, ddof=1)
index += 1
# for col in np.nditer(separate_class, order='F'):
# temp_mean[index] += np.mean(col)
# temp_std[index] += np.std(col, ddof=1)
#
self.mean_dict[label] = temp_mean
self.std_dict[label] = temp_std
@staticmethod
def compute_probabilities(new_list):
num_samples = float(len(new_list))
# Creating a dictionary of counters to store the count of each label. Label is stored as the key in the dict
target_probs = dict(Counter(new_list))
for label_key in target_probs.keys():
target_probs[label_key] /= num_samples
return target_probs
@staticmethod
def gaussian_probability(x, mean, std):
if std == 0:
return 1
exp = math.exp(-(math.pow(x-mean, 2)/(2*math.pow(std, 2))))
return (1 / (math.sqrt(2 * math.pi) * std)) * exp
def predict(self, X):
test_rows, test_cols = np.shape(X)
print(test_rows, test_cols)
predictions = np.zeros(test_rows)
for t in range(0, test_rows):
test_label_probs = {}
for l in labels:
prob_of_label = self.label_probabilities[l]
likelihood = 0
for c in xrange(0, test_cols):
# print(c)
x = X[t][c]
mean = self.mean_dict[l][c]
stdev = self.std_dict[l][c]
res = abs(self.gaussian_probability(x, mean, stdev))
# print(res)
likelihood += math.log(res) if res > 0.0 else 0
likelihood += math.log(abs(prob_of_label)) if abs(prob_of_label) > 0.0 else 0
test_label_probs[l] = likelihood
# print("l:", l, test_label_probs)
predictions[t] = max(test_label_probs.iteritems(), key=operator.itemgetter(1))[0]
# print(predictions)
return predictions
# return a numpy array of predictions
| [
"jhambneha30@gmail.com"
] | jhambneha30@gmail.com |
74b05663dfd6a799c1fee801ffbf488de14167c8 | c57bc3dd44198414c7ec3db45598a607f5389edb | /config.py | 79372b8bbb97886b81b40af36a7272a60ba1aae1 | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | maozcan/youtube-dl-server | 368f94e1a8a4fdcc5ecce2d76281cd863077bcc0 | db35eefab908ae834eef1b145fb1786345093cda | refs/heads/master | 2023-05-12T09:53:56.835467 | 2021-06-06T23:15:03 | 2021-06-06T23:15:03 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 329 | py | import os
# Check for legacy DB to not break anything
if (os.path.exists('./youtube-dl-server-database.db')):
DATABASE_PATH = ('./youtube-dl-server-database.db')
else:
DATABASE_PATH = os.path.join('db' + os.sep + 'youtube-dl-server-database.db')
# LEGACY DATABASE_PATH
#DATABASE_PATH = ('./youtube-dl-server-database.db') | [
"2100452+flatline-84@users.noreply.github.com"
] | 2100452+flatline-84@users.noreply.github.com |
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